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About the Role The Data Migration / Integrations Specialist will own the operational and technical work required to move customers onto Opendate and keep key integrations running reliably. This role sits at the intersection of customer onboarding, data operations, support, product, and engineering. You will work with exports from legacy systems, clean and validate data, import events, customers, orders, donations, holds, ticket types, seating-related data, marketing opt-ins, and historical records, and help troubleshoot integrations with systems like POS, marketing platforms, CRM tools, reporting tools, and third-party ticketing/distribution partners. This is a great fit for someone who is detail-oriented, technically curious, comfortable with messy data, and able to communicate clearly with both internal teams and customers. What You’ll Do Data Migration & Customer Onboarding - Own customer data migration projects from kickoff through QA and launch. - Review customer exports from legacy ticketing, CRM, POS, and marketing systems. - Clean, normalize, and map data into Opendate’s structure. - Import and validate records such as: - Events and recurring events - Ticket types and pricing - Customers, fans, and contacts - Orders, donations, and transaction history - Marketing opt-ins and audience segments - Holds, comps, guest lists, and venue-specific notes - Seating-related data where applicable - Build repeatable migration checklists, QA processes, and rollback plans. - Identify data inconsistencies before they become customer-facing issues. - Partner with customer success and implementation teams to ensure customers understand what was imported, where it lives, and how to use it. Integrations Support - Help configure, monitor, and troubleshoot integrations across Opendate’s ecosystem. - Support integrations involving systems such as POS, marketing tools, CRM platforms, calendars, reporting tools, SMS/email tools, advertising pixels, and distribution partners. - Investigate sync failures, missing records, duplicate records, broken mappings, stale data, and API/webhook issues. - Work with engineering to diagnose root causes and escalate issues with clear reproduction steps, logs, examples, and business impact. - Help define integration requirements based on real customer workflows. - Create internal documentation for common integration setup and troubleshooting paths. Data Operations & Production Support - Run and validate approved scripts, imports, backfills, and data correction workflows. - Use SQL, CSV tooling, admin tools, logs, and internal dashboards to investigate support escalations. - Help resolve production data issues involving events, orders, ticket counts, opt-ins, payouts, reports, imports, and sync jobs. - Create repeatable processes for common one-off fixes so they become less dependent on engineering. - QA migration and integration work in staging and production environments. - Maintain a high bar for data accuracy, customer communication, and operational safety. Process & Documentation - Build migration playbooks for common source systems. - Maintain field mapping documentation and import templates. - Document known edge cases, validation rules, and customer-specific requirements. - Create internal guides for customer success, support, and implementation teams. - Recommend product/admin tooling improvements that reduce manual migration and support work over time. What We’re Looking For - 2+ years of experience in data operations, technical implementation, integrations support, customer onboarding, solutions engineering, technical support, or a similar role. - Strong comfort working with spreadsheets, CSVs, exports, messy customer data, and structured records. - Ability to investigate technical issues across APIs, logs, dashboards, admin tools, and customer reports. - Strong attention to detail and comfort validating large sets of data. - Clear written communication and strong ownership of follow-through. - Ability to work with both technical and non-technical stakeholders. - Comfort operating in a fast-moving startup environment where priorities can shift quickly. - Strong judgment around production data, customer impact, and when to escalate. Nice to Have - Experience with ticketing, live events, venues, promoters, concerts, festivals, or event operations. - Experience migrating data from legacy SaaS platforms. - Experience with Ruby on Rails applications or reading application code. - Experience with Postgres, BigQuery, Looker, Metabase, Retool, or similar tools. - Experience with APIs, webhooks, OAuth, background jobs, or integration monitoring. - Experience with platforms such as Twilio, Stripe, Toast, Clover, Meta Ads / Conversions API, Google Calendar, HubSpot, or similar systems. - Experience supporting seating charts, ticketing inventory, orders, payouts, CRM records, or marketing automation. - Basic scripting ability in Ruby, Python, or JavaScript. You’ll Be Successful If You - Can take a messy customer export and turn it into clean, usable data in Opendate. - Catch problems before customers do. - Communicate clearly when something is blocked, risky, incomplete, or ready for launch. - Make repeatable processes out of one-off migration work. - Reduce the amount of migration and integration work that has to go directly to engineering. - Build trust with customer success, support, product, and engineering teams. - Care about getting the details right because small data issues can create real customer pain. Example Projects - Import historical customers, orders, donations, and marketing opt-ins from a legacy ticketing platform. - Troubleshoot why POS sales did not sync into an event report. - Validate ticket counts and order data after a migration. - Clean and import a venue’s upcoming event calendar. - Help configure SMS, email, advertising, or CRM-related integrations. - Investigate duplicate records, missing transaction history, or incorrect reporting totals. - Create a reusable migration checklist for a new legacy system. - Work with engineering to define better admin tools for import rollback, validation, and customer data review. Why Join Opendate You will play a critical role in helping venues and promoters move onto Opendate successfully. This role has direct customer impact, high visibility across the company, and the opportunity to shape the way Opendate scales onboarding, integrations, and data operations. If you like solving messy real-world data problems, working across teams, and making complex customer transitions feel simple, we’d love to talk.
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Role Description Invicta Software is seeking a detail-oriented and reliable Data Entry Assistant to join our dynamic team. This role is crucial in ensuring accurate and timely data management, supporting various departments to maintain high-quality information flow. If you have a keen eye for detail and enjoy working in a fast-paced environment, this opportunity offers a chance to contribute directly to our operational success. - Accurately input, update, and maintain data across multiple platforms and databases. - Support the team by verifying data integrity and resolving discrepancies. - Assist with organizing and managing digital and physical records to ensure accessibility and compliance. - Enter data from various sources into company systems with high accuracy and speed. - Review and verify data for completeness and correctness before submission. - Identify and correct errors or inconsistencies in data entries. - Maintain confidentiality and security of sensitive information. - Collaborate with team members to streamline data entry processes and improve efficiency. - Assist in generating reports and summaries as needed. - Perform routine backups to ensure data preservation. Qualifications - High school diploma or equivalent; additional certification in data management or related fields is a plus. - Proven experience in data entry or administrative support roles. - Proficiency with Microsoft Office Suite, especially Excel, and familiarity with database systems. - Strong attention to detail and ability to spot errors quickly. - Excellent organizational and time management skills. - Ability to work independently and as part of a team. - Good communication skills and a professional attitude. Benefits - Competitive salary and performance-based incentives. - Comprehensive health, dental, and vision insurance plans. - Opportunities for professional development and career growth. - Supportive and inclusive work environment. - Flexible work schedules and remote work options. - Paid time off and holiday benefits.
Role Description We are looking for a Corporate Data Architect who will help organize and standardize how the organization understands and uses data. Your main task will be to design and develop data architecture at the level of the entire organization so that data is consistent, well defined, and ready to be used in analytics, AI, and system integrations. You will act as a bridge between the worlds of business, data, and technology, connecting strategy, standards, and practice. - Design semantic, logical, and conceptual data models based on business domains and organizational processes. - Define and standardize the corporate data model as part of the Data Governance initiative. - Create and maintain reference architecture (logical and technical reference architectures) and design patterns for data domains. - Co-create the strategy and roadmap for data architecture development that supports the vision of the Data Governance program and organizational goals. - Collaborate with Data Governance, Data Management, Analytics, AI, and Data Engineering teams to integrate models with the data catalog and metadata management tools. - Support technical teams in implementing models in data layers. - Participate in designing data integration, flow, and quality processes (data lineage, data contracts, data quality). - Identify and certify authoritative data sources within organizational domains. - Monitor the compliance of data solutions with Data Governance policies and standards as well as architectural recommendations. - Support the resolution of data conflicts and issues. - Create architectural documentation, metamodels, and inter-domain relationship diagrams. - Consult on and develop good practices in the area of modeling, metadata, and information architecture. - Collaborate on defining the vision, priorities, and directions for the development of data architecture in the organization. Qualifications - Experience in data modeling (conceptual, logical, physical), preferably in a large organization or within complex data ecosystems. - Practical knowledge of tools and methodologies such as ER, UML, IDEF1X, Data Vault, 3NF, Kimball/Inmon. - Ability to work with data catalogs and metadata management tools (e.g. DataHub, Collibra, Alation, Atlan). - Knowledge of SQL and relational data models. - Experience working with database systems and data architecture in a cloud environment (AWS). - Ability to work with engineering and analytics teams to translate business needs into data models. - Understanding of data security, availability, and control principles. - Good command of English (reading documentation, community discussions). Requirements - Experience in Data Governance, Data Quality, Master Data, and Metadata Management projects. - Knowledge of concepts and technologies related to the semantic data layer: ontologies, RDF, OWL, GraphQL, dbt Semantic Layer, Semantic Kernel. - Knowledge of Python for automation, model validation, and API integrations. - Knowledge of event-driven data architecture. - Experience in creating and maintaining architectural documentation. Benefits - Flexible employment and remote work. - International projects with leading global clients. - International business trips. - Non-corporate atmosphere. - Language classes. - Internal & external training. - Private healthcare and insurance. - Multisport card. - Well-being initiatives.
Trilon Group provides smart and sustainable infrastructure solutions across transportation, water, energy, environment, and community sectors. The firm offers a
Data Engineer Department: IT Job Description: Employment Type: Full Time Location: Remote- USA Compensation: $116,000 - $155,000 / year Description Trilon is building a supercharged, technology-enabled future for our people and partners. The Data Engineer plays a key role in that mission by building and maintaining the data platform that powers Trilon's enterprise analytics, automation, and AI capabilities. Reporting to the Vice President, Data & DevOps, this role is responsible for designing, developing, and maintaining scalable data integrations and transformations in Azure and Microsoft Fabric. The Data Engineer ensures that Trilon's data platform delivers reliable, high-quality, and well-structured data to support business intelligence, operations, and innovation. This role serves as the primary custodian of Trilon's integrated data model and is instrumental in developing a unified, extensible architecture that scales with continued acquisitions. The Data Engineer designs and builds secure Power BI semantic models for consumption by analysts and decision-makers, ensuring consistent and governed access to enterprise data. This role also partners closely with the AI and Innovation vTeam to prepare data for analytics, machine learning, and retrieval-augmented generation (RAG) applications. Key Responsibilities Data Platform Engineering and Maintenance - Serve as the primary owner and technical steward of the Trilon enterprise data platform - Design, develop, and maintain data pipelines and workflows using Azure Data Factory, Synapse, and Microsoft Fabric - Build and manage data transformations, orchestration, and automation across structured, semi-structured, and unstructured data sources - Ensure scalability, reliability, and performance of the data platform as Trilon continues to grow through acquisition - Implement monitoring and alerting to proactively detect and resolve pipeline or data quality issues Data Integration and Modeling - Develop and maintain integrations between Trilon's enterprise systems, cloud services, and acquired partner environments - Design and maintain a unified, scalable data model that harmonizes data across business systems - Build secure, governed, and high-performance Power BI semantic models optimized for analytics and self-service reporting - Collaborate with business analysts and data consumers to ensure data models support enterprise reporting needs and KPIs - Partner with cybersecurity and infrastructure teams to ensure data models and access patterns meet compliance and governance standards Data Quality and Governance - Implement validation and quality checks to ensure accuracy, completeness, and timeliness of enterprise data sets - Maintain metadata, lineage, and documentation to promote transparency and reusability - Define and enforce data quality and consistency standards across all integrated sources - Collaborate with the Technology Asset Manager and Service Platform Manager to align system integrations and data governance - Support data cataloging, discovery, and classification initiatives within Microsoft Purview or equivalent tools Automation, Optimization, and Resilience - Develop automated frameworks for ingestion, transformation, and validation using Azure-native tools and pipelines - Implement DevOps principles for data workflows including version control, testing, and deployment automation - Optimize pipeline performance, resource utilization, and data freshness - Build resilience and fault tolerance into data operations to ensure reliability and recovery - Create reusable components and templates to streamline integration of new data sources and partner systems AI and Innovation Enablement - Collaborate with the AI and Innovation vTeam to prepare and structure data for AI, ML, and RAG-based applications - Develop and maintain data pipelines that support model training, evaluation, and fine-tuning - Curate and transform unstructured data for retrieval, embedding, and vectorization within AI applications - Ensure data readiness for generative AI tools, chat interfaces, and knowledge retrieval systems - Stay informed of emerging AI data engineering trends and Microsoft Fabric AI integrations Collaboration and Cross-Domain Partnership - Partner with application and infrastructure teams to ensure reliable and secure data exchange across systems - Collaborate with business stakeholders and analysts to understand reporting needs and deliver usable data models - Support integration engineers in onboarding new firms and ensuring their data aligns with Trilon's enterprise model - Work closely with cybersecurity and compliance teams to enforce data protection, retention, and access policies - Provide documentation, architecture diagrams, and operational standards for the data platform and pipelines Skills, Knowledge and Expertise - 5 or more years of experience in data engineering, data integration, or data platform development - Strong hands-on experience with Azure Data Factory, Azure Synapse, Microsoft Fabric, and related Azure data services - Proficiency in SQL, DAX, Power Query, and data modeling for Power BI - Experience designing and maintaining Power BI semantic models, datasets, and row-level security configurations - Familiarity with data governance, cataloging, and lineage management in tools like Microsoft Purview - Experience building and optimizing cloud data pipelines with structured, semi-structured, and unstructured data - Understanding of data preparation for AI and machine learning applications, including RAG architectures - Exposure to engineering and geospatial data such as CAD, BIM, and GIS - Strong analytical and problem-solving skills with a focus on scalability and performance - Excellent collaboration and communication skills across technical and business audiences - Bachelor's degree in Computer Science, Data Engineering, or related field preferred - Microsoft certifications such as Azure Data Engineer Associate or Fabric Analytics Engineer Associate are a plus - May require occasional travel to Trilon offices or partner locations for integration or collaboration activities About Trilon Trilon was formed with the vision of building the next Top 20 infrastructure consulting firm in North America by bringing together some of the nation's best infrastructure consulting firms, focused on delivering practical and sustainable infrastructure solutions. Trilon is backed by Alpine Investors, a PeopleFirst Private Equity Firm. Trilon currently comprises 5,500+ staff across the US. For more information, visit www.trilon.com. Pay Transparency The base salary range for this role is indicated in the posting. This range reflects the company's good faith estimate of the compensation for this position at the time of posting. Final compensation will be determined based on factors such as experience, skills, qualifications, internal equity, and geographic location.
• We’re looking for a Staff Data Engineer to join a multi-disciplinary engineering team building modern, enterprise-grade data platforms. • In this role, you will design scalable data lakes, warehouses, and pipelines, define governance and quality standards, and drive data platform modernization across real, in-flight work where performance, reliability, and security are critical. • You’ll mentor more junior engineers, partner with leadership on data strategy, and bring an AI-forward mindset. • Define data architecture and platform strategy, leading design across pipelines, warehouses, and data lakes. • Build and optimize scalable data pipelines supporting batch and real-time processing. • Define and enforce data governance, quality standards, and compliance frameworks across the platform. • Build monitoring, logging, and alerting for data pipelines and services, and contribute to CI/CD workflows for data deployment and automation. • Drive data platform modernization, optimizing for performance, cost, and scalability. • Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams. • Lead the design and implementation of data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows. • Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption.
We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other protected characteristic.
Role Description We’re seeking a Mid-Level Data Engineer/Analyst to independently design, build, and optimize data pipelines and analytics solutions that power business intelligence and AI/ML initiatives. In this role, you will own key data workstreams end to end, build production-grade transformation layers using dbt and Spark, manage data infrastructure on Snowflake and Databricks, and collaborate with analysts, data scientists, and product teams to deliver reliable, well-governed, and high-quality data products. You will also contribute to the maturity of our DataOps and data observability practices. - Design, build, and maintain production-grade ETL/ELT pipelines using dbt, Apache Spark (PySpark), Airflow, Dagster, or Prefect. - Develop and optimize data models on Snowflake, Databricks, BigQuery, or Redshift following dimensional modeling, data vault, or One Big Table patterns. - Implement and manage data ingestion from diverse sources including databases, REST/GraphQL APIs, event streams (Kafka, Kinesis), SaaS platforms, and flat files using Fivetran, Airbyte, or custom connectors. - Build and maintain semantic/metrics layers and curated data products for analytics, reporting, and self-service consumption. - Implement data quality, testing, and observability frameworks using dbt tests, Great Expectations, Soda, Monte Carlo, or Datafold. - Create advanced dashboards, reports, and analytical visualizations using Tableau, Looker, Power BI, or Sigma Computing. - Optimize query performance, pipeline efficiency, and cloud data platform costs across Snowflake, Databricks, or BigQuery. - Collaborate with data scientists and ML engineers to prepare and serve feature datasets for machine learning models. - Implement DataOps practices including CI/CD for data pipelines, version-controlled transformations, and automated testing. - Write production-quality Python and SQL code with proper testing, documentation, and error handling. - Support data governance initiatives including cataloging, lineage tracking, access controls, and PII management using tools like Alation, Atlan, DataHub, or Unity Catalog. Qualifications - Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. - 3–5 years of professional experience in data engineering, analytics engineering, or a closely related role with production delivery. - Strong proficiency in SQL and experience writing complex transformations, window functions, CTEs, and performance-tuned queries. - Hands-on experience with at least one modern data platform: Snowflake (strongly preferred), Databricks, BigQuery, or Redshift. - Experience with dbt (data build tool) for data transformation, testing, and documentation in production environments. - Working knowledge of Python (Pandas, PySpark, or Polars) for data processing and pipeline development. - Experience with workflow orchestration tools: Airflow, Dagster, Prefect, or cloud-native equivalents (AWS Step Functions, Azure Data Factory). - Familiarity with data ingestion tools and patterns: Fivetran, Airbyte, CDC (Debezium), or streaming ingestion (Kafka, Kinesis). - Experience with data visualization and BI tools: Tableau, Looker, Power BI, or Sigma. - Understanding of data modeling methodologies (Kimball, Data Vault, OBT) and data warehousing best practices. - Familiarity with version control (Git), CI/CD for data, and Agile development workflows. Preferred Qualifications - Snowflake SnowPro Core, Databricks Data Engineer Associate, or AWS Data Analytics Specialty certification. - Experience with Apache Spark and Databricks for large-scale data processing and lakehouse architectures. - Familiarity with data cataloging and governance tools: Alation, Atlan, DataHub, Collibra, or Databricks Unity Catalog. - Experience with data observability platforms: Monte Carlo, Datafold, Soda, or Elementary. - Exposure to streaming data pipelines using Kafka, Spark Structured Streaming, Flink, or Kinesis. - Experience with metrics/semantic layers: dbt Semantic Layer, Cube, or Looker Modeling Language (LookML). - Knowledge of cloud data infrastructure: AWS (S3, Glue, Athena, Redshift, Lake Formation), Azure (ADLS, Synapse, Data Factory), or GCP (GCS, Dataflow, BigQuery). Benefits - Unlimited PTO. - Very generous parental leave, much above industry standards. - Entrepreneurial culture where pushing limits and taking risks is everyday business. - Open communication with management and company leadership. - Small, dynamic teams = massive impact. - Medical, Dental and Vision coverage for employees. - Access to Disability & Life insurance. - Mental health and wellbeing support. - Annual bonus program. - Employer Stock Purchase Program (ESPP). - Yearly Team building experiences. - Mentorship and sponsorship opportunities. - Manager resources and support.
Role Description We are looking for a Data Science Specialist to join our product-focused engineering team. In this role, you will work closely with Data Scientists, ML Engineers, and domain experts to analyze complex datasets, understand model behavior, and drive data-centric improvements that enhance machine learning performance. Qualifications - Master's or PhD degree in Data Science, Computer Science, Applied Mathematics, Statistics, Physics, or a related field - 2+ years of hands-on experience working with machine learning datasets - Experience with time-series, sensor, image, or video data - Strong Python skills and experience with NumPy, pandas, matplotlib, and seaborn - Experience with dimensionality reduction and representation analysis techniques such as UMAP, t-SNE, and PCA - Solid understanding of machine learning fundamentals, model evaluation, and diagnostics - Experience supporting both traditional machine learning and deep learning projects Requirements - Experience working with sensor data, including radar, magnetic, environmental, 3D, or IoT datasets - Familiarity with scikit-learn preprocessing workflows - Experience handling imbalanced datasets, noisy labels, sensor noise, and data drift - Knowledge of model interpretability, feature importance, and embedding analysis - Experience working with data annotation and labeling teams - Familiarity with MLflow, Weights & Biases, DVC, or similar tools Responsibilities - Data Understanding and Representation Analysis - Analyze high-dimensional sensor and feature datasets using UMAP, t-SNE, PCA, and similar techniques - Identify clusters, anomalies, blind spots, distribution gaps, and class or environment mismatches - Diagnose dataset shift, domain drift, sparsity, and representation collapse - Model-Aware Data Analysis - Perform data analysis aligned with classical ML models including XGBoost, SVR, k-NN, and tree-based models - Support analysis for deep learning models such as CNNs and Transformers - Analyze embeddings, confusion matrices, and model failure patterns to trace errors back to data issues - Data Quality and Curation - Investigate imbalanced data, noisy sensor signals, mislabeled samples, and ambiguous cases - Develop approaches for improving weakly labeled or unlabeled data including clustering and pseudo-labeling - Perform data mining on large collections of field data to extract insights and patterns - Design processes for converting noisy or partially verified data into high-quality validated datasets - Insight-Driven Improvements - Translate exploratory findings into clear recommendations for data filtering, relabeling, or new data collection - Advocate for and implement data-centric improvements to enhance model robustness - Work closely with engineering teams to integrate improved data workflows into ML pipelines Benefits - Health insurance from the first days, regardless of the probationary period - Christmas holidays from December 25 to December 31 - Cooperation with Superhumans center and Veteran HUB - Support for psychological counseling provided by the Veteran Hub - Internal policy making the company friendly to military and veterans
Role Description As a Business Data Engineer at Gainwell, you can contribute your skills as we harness the power of technology to help our clients improve the health and well-being of the members they serve — a community’s most vulnerable. Connect your passion with purpose, teaming with people who thrive on finding innovative solutions to some of healthcare’s biggest challenges. - Become a healthcare insurance subject matter expert with sufficient technical skills to produce proof-of-concept solutions to various issues within the industry. - Perform data cleaning and analysis using SQL, Python, and AI models. - Analyze big data from diverse sources to identify trends, address gaps, and discover improvement opportunities that align with improving the healthcare system. - Provide subject matter expertise in identifying product uses and designing strategies that address significant business and technical challenges. - Gather and translate business requirements into analytical applications and advanced dashboard visuals, reports, or other alert systems. - Document code using documentation tools and participate in collaborative team code reviews. - Collaborate with various teams within the company to advise on technical solutions. - Stay informed on industry trends, best practices, and innovations, ensuring competitive market relevance of company products. Qualifications - 2+ years of experience in Data-Focused roles OR 2+ years in Industrial/Manufacturing Engineering. - Experience in a Database Query Language: Oracle, MySQL, SQL Server, DB2. - Experience with any of the following Scripting Languages: Python, R, or JavaScript. Python Preferred. - Experience working with Data Visualization tools such as Tableau or Power BI. - Microsoft Office Products and Access Proficient. Requirements - Fully Remote Opportunity within the US. - Required Travel 0-10%. - Videos cameras must be used during all interviews, as well as during initial week of orientation. - The Deadline to submit applications for this posting is July 28, 2026. Benefits - Generous, flexible vacation policy. - 401(k) employer match. - Comprehensive health benefits. - Educational assistance. - Leadership and technical development academies to help build your skills and capabilities.
The not-for-profit Cleveland Clinic was established in 1921 by four prominent doctors and provides a unique combination of clinical and medical care with academ
Role Description Join the Cleveland Clinic team, where you will work alongside passionate caregivers and provide patient-first healthcare. As a Data Registry Coordinator, you will: - Collect, validate and submit reliable data to the clinical registries while identifying opportunities for refinement. - Provide content expertise for program and registry requirements and guidelines to clinical teams and committees in a multi-hospital environment. - Identify patients in the clinical registries through the application of strict criteria and protocols. - Collect and validate data for the program using the applicable criteria and definitions established by the registry. - Establish and maintain adequate workflow for data collection. - Provide accurate and timely submission of data into the program's website and assure the transmission of completed data according to the program's targets and deadlines. - Analyze data and reports to identify opportunities for improvement. - Collaborate with clinical departments/units on performance improvement initiatives. - Perform other duties as assigned. Qualifications - Bachelor's Degree in Science, Healthcare or a related field and three years of clinical experience or related research/registry experience OR a diploma from an accredited school of nursing or a certification from an Allied Healthcare program and five years of experience OR Associate’s degree in Science, Healthcare or a related field and five years of experience. - Familiarity with medical record documentation and mainframe systems for patient information. Requirements - Bachelor of Science in Nursing (preferred). - Current valid state Registered Nurse (RN) license may be required for some positions (preferred). - Previous transplant experience (preferred). - Ability to perform work in a stationary position for extended periods. - Ability to operate a computer and other office equipment. - Ability to communicate and exchange accurate information. - In some locations, ability to move up to 10 pounds. Benefits - Support and appreciation from the Cleveland Clinic team. - Opportunity to build a rewarding career with one of the most respected healthcare organizations in the world.
Role Description We are currently seeking a Senior Big Data Engineer in the Hadoop Platform. Our ideal candidate exhibits a can-do attitude and approaches his or her work with vigor and determination. Candidates will be expected to demonstrate excellence in their respective fields, possess the ability to learn quickly, and strive for perfection within a fast-paced environment. - Selecting and integrating any Big Data tools and frameworks required to provide requested capabilities - Implementing ETL processes using Spark - Ensure optimization of software through design reviews and code reviews - Monitoring performance and advising any necessary infrastructure changes & defining data retention policies - Management of Hadoop cluster, with all included services - Building stream-processing systems, using solutions such as Storm or Spark-Streaming - Solve any ongoing issues with operating the Hadoop cluster Qualifications - Proficient understanding of distributed computing principles - Proficiency with Hadoop, MapReduce, HDFS - Good knowledge of Big Data querying tools, such as Pig, Hive, and Impala - Strong understanding of Data Structures and Algorithms - Experience with integration of data from multiple data sources - Experience with various messaging systems, such as Kafka or RabbitMQ - Experience in scripting languages such as Python - Experience with Cloudera/MapR/Hortonworks Apache HDFS - Experience in the field of Automotive Telematics Software is a big plus - Experience with scripting tools and methods to optimize SW development and testing activities - At least 4-7+ years of industry experience in Big Data Domain Education - MS/BS minimum in the areas of Computer Science, Computer Engineering, or other related fields or equivalent experience Contact Information Regards, Niranjan | Manager - Business Development & Staffing - North America niranjan.m@aciinfotech.com
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Python, SQL, AI, Data Engineering, Power BI, AI/ML
