Scientist III, Data Engineer Location Remote, Massachusetts, United States of America Job Id R-01349037 Job Type Full time Category IT, Data & Tech Remote Fully Remote Title: Scientist III, Data Engineer Location: Remote Massachusetts United States of America Job Description: Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description COMPANY: Thermo Fisher Scientific Inc. LOCATION: 168 Third Ave., Waltham, MA 02451 TITLE: Scientist III, Data Engineer HOURS: Monday to Friday, 8:00 am to 5:00 pm DUTIES: Develop scalable data pipelines and build out new API integrations to support continuing increases in data volume and complexity. Own and deliver Projects and Enhancements associated with Data platform solutions. Develop solutions using PySpark/EMR, SQL and databases, AWS Athena, S3, Redshift, AWS APIT Gateway, Lambda, Glue, and other Data Engineering technologies. Write Complex Queries and edit them as required for implementing ETL/Data solutions. Implement solutions using AWS and other cloud platform tools, including GitHub, Jenkins, Terraform, Jira, and Confluence. Follow agile development methodologies to deliver solutions and product features by following DevOps, Data Ops, and Dev Sec Ops practices. Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, and re-designing infrastructure for greater scalability. Travel: Up to 5% travel required (domestic and international). Can work remotely or telecommute. REQUIREMENTS: MINIMUM Education Requirement: Master’s degree or foreign degree equivalent in Technology Management, Information Technology, Computer Science, or related field of study. MINIMUM Experience Requirement: 3 years of experience as a Data Developer, Data Engineer, or related occupation. Alternative Education and Experience Requirement: Bachelor’s degree or foreign degree equivalent in Technology Management, Information Technology, Computer Science, or related field of study plus 5 years of experience as a Data Developer, Data Engineer, or related occupation. Required knowledge or experience with: Full life cycle implementation in AWS using PySpark/EMR, Athena, S3, Redshift, AWS API Gateway, Lambda, and Glue; Agile development methodologies following DevOps, Data Ops, and Dev Sec Ops practices; ETL Pipelines, GitHub, Jenkins, Terraform, Jira, Bitbucket, and Confluence; Informatica, Databricks, & AWS Glue; Data Lake using AWS Databricks, Apache Spark, & Python; Data visualization tools like PowerBI and Tableau; Data modeling and optimization for OLAP/OLTP systems with Star/Snowflake schemas; Strong knowledge of SQL, query optimization, and performance tuning in Redshift, Snowflake, or Oracle; Experience with CI/CD pipelines for data workflows using Jenkins, GitHub Actions, or AWS CodePipeline; Data governance, cataloging, and lineage using tools such as AWS Glue Data Catalog, Collibra, or Alation; Implementing data security, encryption, IAM policies, and compliance regulatory frameworks; Batch and real-time streaming pipelines using Kafka and Spark Streaming; Managing data governance, access control, and lineage using Databricks Unity Catalog for secure enterprise data sharing; Implementing Delta Lake architecture for ACID transactions, schema enforcement, and scalable data pipelines; Optimizing Delta Live Tables for automated ETL orchestration and reliable data delivery; Ensuring high availability and SLA-driven production support with proactive monitoring, incident management, and root cause analysis; Collaboration with cross-functional teams to translate scientific, laboratory, and business requirements into scalable data solutions. Salary: $161803 to $186000 per year Compensation and Benefits The salary pay range estimated for this position Scientist III, Data Sciences based in Massachusetts is $161,803.00–$186,000.00. This position may also be eligible to receive a variable annual bonus based on company, team, and/or individual performance results in accordance with company policy. We offer a comprehensive Total Rewards package that our U.S. colleagues and their families can count on, which includes: - A choice of national medical and dental plans, and a national vision plan, including health incentive programs - Employee assistance and family support programs, including commuter benefits and tuition reimbursement - At least 120 hours paid time off (PTO), 10 paid holidays annually, paid parental leave (3 weeks for bonding and 8 weeks for caregiver leave), accident and life insurance, and short- and long-term disability in accordance with company policy - Retirement and savings programs, such as our competitive 401(k) U.S. retirement savings plan - Employees’ Stock Purchase Plan (ESPP) offers eligible colleagues the opportunity to purchase company stock at a discount For more information on our benefits, please visit: https://jobs.thermofisher.com/global/en/total-rewards

It’s one thing to deliver a technical project. It’s quite another to delight a customer while doing it.    About Us   Ascent has recently been acquired by Acuity Analytics.  This is both a significant milestone for us and a tremendous opportunity for you.  Acuity Analytics is a business with a strong global reputation, an impressive client base and ambitious growth plans.  We delivers deep insights and domain-led digital transformation to high-growth and heavily regulated organisations.  To our customers, we bring a partnership that provides the talent, technology and capability to enhance performance and operational efficiency. About the role   In this role, you will support the development of a data pipeline that aggregates data from multiple sources and prepares structured inputs for GenAI‑based processing. Skills and Requirements Required Skills - - Develop Python-based connectors to ingest data from APIs and other data sources - Perform data preparation, transformation, and consolidation - Support integration of prepared data into GenAI workflows - Assist with generating summaries and structured outputs via LLMs - Ensure code quality, scalability, and maintainability - Tech stack: - Python development experience - API integration and data engineering - Working with LLMs / GenAI use cases - Cloud-based data & AI architectures (AKS) Why join us People are at the Heart of our Business. By investing in people, we achieve exceptional results for our clients and create new opportunities for our teams to thrive. Check out this page for more details. If you have any questions contact our Talent Acquisition team on ta.admin@acuityanalytics. For more details about life at Ascent, check out our Life Page here. For more details about Acuity Analytics please see here: Read here.

We are seeking a skilled and motivated Analytics / Data Engineer to join our IT Enterprise Solutions team. This role is ideal for someone with a strong foundation in SQL Server, Oracle Databases, cloud data platforms (Azure and/or AWS), and business intelligence, who thrives on building reliable data pipelines and transforming data into trusted, analytics-ready models for reporting and decision-making. The ideal candidate brings hands-on experience with Power BI (including semantic models, DAX, and data modeling) and a passion for delivering high-quality, scalable, and governed analytics solutions that support enterprise reporting, dashboards, and self-service BI. Responsibilities Data Engineering & Integration - Design, develop, and maintain robust data pipelines and ETL/ELT processes using Azure Data Factory, SSIS, or similar tools. - Support and enhance data integrations between enterprise applications and external data sources (batch and near real-time). - Manage and optimize Azure SQL PaaS and on-prem SQL Server and Oracle Database environments for performance, scalability, and security. - Collaborate with application teams to ensure seamless data feed operations and troubleshoot integration issues. - Participate in DevOps and CI/CD workflows for data and analytics solutions. Analytics Engineering & Business Intelligence - Develop, optimize, and govern Power BI semantic models (datasets), including relationships, measures, and calculated columns. - Write and optimize DAX measures to support complex business logic and performance-efficient reporting. - Develop and maintain Power BI dashboards and reports, ensuring usability, performance, and consistency across the organization. - Partner with business stakeholders to translate reporting requirements into scalable, reusable analytics assets. - Implement and enforce Power BI governance practices, including workspace design, dataset reuse, security (RLS), and deployment standards. Data Governance, Quality & Security - Implement and maintain data governance, data quality, lineage, and documentation practices. - Ensure compliance with data security, privacy, and access control standards across data and analytics platforms. - Support auditing, monitoring, and certification of enterprise datasets and reports. - Develop and maintain operational runbooks covering: - Break/fix procedures - Refresh restarts and failure remediation - Access provisioning and troubleshooting - Participate in ITIL-aligned processes including incident, problem, change, and release management. - Performs other related duties as assigned. Qualifications - Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field. - Applicants must be authorized to work in the United States without the need for current or future visa sponsorship. - Must reside in Eastern Time Zone. - 3+ years of experience in data engineering, analytics engineering, or business intelligence roles. - Strong proficiency in SQL Server, Oracle, T-SQL, and Azure SQL PaaS. - Hands-on experience with Power BI, including: - Semantic model (dataset) design - DAX measures and calculations - Report and dashboard development - Solid understanding of data modeling, dimensional modeling, and data warehousing concepts. - Experience supporting application data integrations and analytics use cases. - Strong performance tuning skills for both SQL queries and Power BI models. - Limited travel may be required. Preferred - Experience with Azure Data Factory, Azure Synapse, Azure Data Lake, or Fabric. - Familiarity with AWS data services (e.g., Redshift, S3, Glue). - Experience with SSIS, SSRS, and SSAS (Tabular). - Exposure to Python or other scripting languages for data transformation and automation. - Experience with Power BI deployment pipelines, workspace governance, and tenant-level best practices. - Familiarity with DevOps and CI/CD pipelines for data and analytics workloads. - Experience with data cataloging, metadata management, and lineage tools. - Security+ certification or ability to obtain a DoD clearance is a plus. About Us Diné Development Corporation (DDC) is a Navajo Nation owned family of companies that provides government agencies and commercial organizations with high-quality IT, professional, environmental, and research and development services. DDC is dedicated to empowering the Navajo Nation and communities we serve. Benefits Eligible full-time employees receive a comprehensive benefits package, including medical, dental, vision, life and disability coverage, retirement savings with company match, paid time off, voluntary supplemental benefits, and access to an employee assistance program. The package also includes educational assistance, with tuition reimbursement. EEO Statement This contractor and subcontractor shall abide by the requirements of 41 CFR 60-1.4(a), 60-300.5(a) and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity, national origin, or for inquiring about, discussing, or disclosing information about compensation, or any other basis prohibited by law. We participate in E-Verify.

The Opportunity You'll own the full data stack for cybersecurity intelligence at QuoIntelligence, building and evolving the pipelines that power our Mercury platform and analyst workflows. This is a role where your architectural decisions have real, visible impact, and where you'll collaborate closely with a small, expert team of two engineers on the data side. Our platform runs on DigitalOcean with a custom-built infrastructure: ZMQ message queues, Docker containers, hand-rolled deployment pipelines, and in-house-maintained Python libraries. It was built before today's managed services existed, and it works because engineers who genuinely understood distributed systems built it from the ground up. The first major project is leading our migration to AWS, transitioning from this custom infrastructure to managed services, without disrupting production. It's the kind of challenge that requires both depth and adaptability: keeping what exists healthy while architecting what comes next. If you thrive in that kind of dual mandate, this role was designed for you. You'll Thrive Here If... You enjoy working across the full stack rather than specializing deeply in one layer. You're energised by inheriting a well-built yet unconventional system and improving it. You're comfortable with ambiguity and find it motivating rather than frustrating. And you want your work to matter not just to a ticket queue, but to the analysts and products that rely on it every day. This is probably not the right fit if your data engineering experience is entirely on managed platforms like Databricks, Snowflake, or BigQuery, or if you're looking for a fully provisioned cloud environment from day one. The AWS migration is the destination you'll help us get there. One more thing: this is an AI-native company. Our products run on AI. We expect engineering to run on it, too. If your relationship with AI stops at asking ChatGPT to explain error messages, this isn't the right fit. What You'll Do - Build and design data pipelines for ingestion, processing, and modeling of cybersecurity intelligence that feeds Mercury and analyst workflows - Partner with Threat Intelligence Engineers on data access patterns, tool integration, and evolving data sources as priorities shift - Shape the team's technical direction: with two engineers, your judgment carries weight - Keep the existing DigitalOcean platform running: manage containers, handle library updates, and debug custom services with confidence - Lead the AWS migration: plan and execute the transition to managed services, with measurable benchmarks at every phase AI-First in Data Engineering AI is an operating principle here. We use AI tools as a core part of how we work. On a lean team maintaining hand-built infrastructure, they're what let us move at the pace of a much larger engineering org. AI as an operating system. You use Copilot, Claude, Cursor (or equivalents) daily to navigate Go services you didn't write, debug custom ZMQ queues, and generate Docker configurations as part of your daily workflow. Evaluate, Integrate, Repeat. Not every AI-generated suggestion belongs in a pipeline processing cybersecurity intelligence data. You test tools against QI's actual problems (migrating custom services to AWS, parsing threat feed data, maintaining manually versioned Python libraries) and drop what doesn't hold up in production. Define success first. Every pipeline feeding Mercury has defined criteria before it ships: ingestion throughput, data freshness, and error rates. We run the AWS migration on measurable benchmarks at each phase, not just "it works on staging." AI accelerates the testing loop, but the loop needs a target. What You'll Bring Must-haves: - Python as your primary language for data engineering work - A solid foundation in pipeline design, you can reason about data from ingestion through modeling - Docker fluency: deploying, debugging, and managing containerized services is routine for you - Experience building custom solutions when no off-the-shelf solution fits, writing the infrastructure yourself, not just assembling managed services - Cloud platform experience (AWS preferred, GCP, or Azure also relevant): enough to architect the target state of a managed-services migration - Microservices architecture or distributed systems: you've designed or maintained service-oriented systems - Working familiarity with Go: you'll read, debug, and modify Go code in some production services. Side projects and CLI tools count; we expect a ramp-up period You'll also need professional proficiency in English (the team works across countries). Nice-to-Haves: - Redis or ZMQ experience for message queuing - Legacy system maintenance: keeping aging infrastructure healthy while building its replacement - Cybersecurity or threat intelligence background Recruitment Process We aim to be as transparent as possible throughout the process and will share updates with you whenever we have progress on our end. To manage your expectations transparently, we have structured the recruitment process as follows: 1. Recruiter Screen 2. Take-Home Assignment 3. Technical Interview 4. Culture Add Interview 5. Offer & Background Check We welcome applications regardless of gender, nationality, ethnic origin, religion, disability, age, or sexual identity. Diversity is key to producing high-quality intelligence.