• Design, build, and operate scalable, reliable data pipelines and data infrastructure • Ensure high-quality data is accessible, trusted, and ready for analytics and data science • Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

Role Description THE CHALLENGE - Design, build, and implement generalized large-scale, sophisticated data pipelines using Nifi for downstream analytics and data science for our Sport Performance products. - Design and develop scalable Nifi ingestion pipelines within AWS cloud services to consume real-time and batch data from external sources. - Ensure the seamless integration of AWS-based tools for data storage, processing, and analytics. - Responsible for ETL development and warehousing using Python and Java. - Create data pipeline triggers and filters within ETL (extract, transform, and load) process to ensure appropriate optimization of data flowing through the system and resource usage. - Implement monitoring and error handling for all new parts of the data pipeline to ensure observability and alerting is available. - Establish rigorous unit testing across the data pipeline to ensure robustness of the system. - Design and create data models for use throughout the ETL system. - Utilize Kafka to efficiently and effectively store data to move throughout the data pipeline and for downstream data science and analytics usage. - Design data architecture and data models for both internal and external representations of data. - Build the data transforms within the data pipeline to convert data from external to internal representations. - Conduct data analytics and debugging of bad data by writing SQL queries. - Build automated cleaning of data to remove bad or unusable data from downstream consumers with logging to understand the frequency and depth of the underlying issues. - Collaborate with other engineering teams to adopt standard methodologies, drive scalability, and increase consistency across systems. - Maintain awareness of company standards and technology guidance; use JIRA, an Agile project management tool, to ensure efficient data development; collaborate with peers to align projects with overall direction. - Follow best practices across Data Engineering to ensure scalable, consistent data architecture and system. - Utilize Java language to build data processor in Nifi framework. - Utilize Docker to ensure consistent, repeatable, and isolated environment for software development and testing. - Work in a self-driven, independent fashion to meet Sport driven deadlines. - Position permits telecommuting from anywhere in the U.S. Qualifications - Master’s degree in Computer Science, Computer Engineering, or closely related field. - 1 year experience as a data engineer or related occupation. - 1 year of experience with: - Python - Java - Kafka - AWS - Docker - ETL Development and Warehousing - Analytic and debugging using SQL - Agile development environment - Designing data architecture Benefits - A collaborative environment with colleagues from all over the world (Offices in Europe, Asia and US) including various social events and teambuilding. - Flexibility to manage your workday and tasks with autonomy. - A balance of structure and autonomy to tackle your daily tasks. - Vibrant and inclusive community, including Women in Tech and Pride groups which welcome all participants. - Global Employee Assistance Programme. - Calm and Reulay app (leading well-being apps designed to support focus, quality rest, mindfulness, and long-term mental resilience). - Online training videos. - Flexible working hours.

Title: Software Engineer (Data Platforms) Location: Midvale, UT, United States Job Description: Zions Bancorporation’s Enterprise Technology and Operations (ETO) team is transforming what it means to work for a financial institution. With a commitment to technology and innovation, we have been providing our community, clients and colleagues the best experience possible for over 150 years. Help us transform our workforce of the future, today. We're looking for a creative Software Engineer (Data Platforms) to build the core of our data governance strategy. In this hands-on role, you'll write code to develop our custom metadata platform, enhance our Governance Portal, and own the technical implementation of best-in-class tools. If you're passionate about building the machinery that enables an organization to trust its data, this is the role for you. The Software Engineer will: - Design, develop, and enhance our core set of microservices and APIs for metadata exchange and governance integration. - Build and maintain integrations between our core platforms, including Collibra, BigID, and our custom-built solutions. - Own the technical operation and enhancement of our core data governance platforms: Collibra (for cataloging), BigID (for discovery and classification). - Contribute to the development of our Governance Portal, a centralized UI that enables self-service workflows for classification, attestation, and compliance. - Engineer and support our customized data profiling solutions to automate discovery and classification across our data lake and EDW. - Collaborate closely with the Enterprise Data Governance product team to translate new standards and policies into technically feasible, automated solutions. - Champion engineering best practices and drive the evolution of our data governance architecture towards a more event-driven, API-first model. Qualifications: - Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience. - 6+ years of professional software engineering experience, with strong proficiency in a language like Python, Java, or Go. A combination of education and experience may meet qualifications. - Proven experience building and consuming APIs and developing microservices. - Solid understanding of SQL and experience with a variety of data stores (e.g., relational databases, data warehouses, data lakes). - Preferred Qualifications - Direct, hands-on experience with a major data governance platform is strongly preferred (Collibra, BigID, Attacama, etc.). - Experience in a data-centric engineering role (Data Platform, Data Governance, or Data Engineering). - Full-stack experience with both back and front end technologies (FastAPI, React, Spring Boot, Django, etc.). - Familiarity with cloud platforms (GCP, AWS, Azure) and infrastructure-as-code principles (Terraform). - A strong sense of ownership and the ability to drive complex projects from conception to production. Visa Sponsorship: This Software Engineer position is currently not eligible for employment visa sponsorship (e.g., H-1B visa). This includes, for example, situations where a candidate may have temporary work authorization while enrolled in school or upon graduation (e.g., CPT, OPT) but would need H-1B visa sponsorship within a few years of employment in order to maintain employment eligibility. Location: This position has a hybrid work from home schedule with a minimum of three days per week in the office at the new Zions Technology Center in Midvale, UT. The Zions Technology Center is a 400,000-square-foot technology campus in Midvale, Utah. Located on the former Sharon Steel Mill superfund site, the sustainably built campus will be the company’s primary technology and operations center. This modern and environmentally friendly technology center will enable Zions to continue to compete for the best technology talent in the state while providing team members with an exceptional work environment with features such as: - Electric vehicle charging stations and close proximity to Historic Gardner Village UTA TRAX station. - At least 75% of the building is powered by on-site renewable solar energy. - Access to outdoor recreation, parks, trails, shareable bikes and locker rooms. - Large modern cafe with a healthy and diverse menu. - Healthy indoor environment with ample natural light and fresh air. - LEED-certified sustainable building that features include the use of low VOC-emitting construction materials. Benefits: - Medical, Dental and Vision Insurance - START DAY ONE! - Life and Disability Insurance, Paid Parental Leave and Adoption Assistance - Health Savings (HSA), Flexible Spending (FSA) and dependent care accounts - Paid Training, Paid Time Off (PTO) and 11 Paid Federal Holidays - 401(k) plan with company match, Profit Sharing, competitive compensation in line with work experience - Mental health benefits including coaching and therapy sessions - Tuition Reimbursement for qualifying employees - Employee Ambassador preferred banking products Apply now if you have a passion for impactful outcomes, enjoy working collaboratively with co-workers, and want to make a difference for the customers and communities we serve.

Senior Data Engineer - Job Identification 14365 - Job Role Data Engineering-Big Data Engineering - Experience (In Years)6-9 - Job Location Seattle Job Description: EXL is hiring a Senior Data Engineer to join a strategic AI / ML platform engagement with a leading specialty retailer. This is a hands-on build role embedded with the client''s platform engineering team. The role requires shipping production-grade data pipelines that feed real-time customer event data into machine learning workflows. The right person is comfortable owning the full lifecycle of pipeline design, build, and deployment: from streaming ingestion through event store design to model-ready feature delivery. This is a high-visibility role with growth potential into a larger book of work as the engagement expands. Required Skills & Experience - 6-12 years of experience in data engineering, platform engineering, or a closely related discipline. - Streaming: Production experience with Kafka consumers and Flink stream processing - building, deploying, and operating streaming jobs at meaningful scale. - GCP Data Stack: Strong SQL on BigQuery (or an equivalent cloud warehouse), with demonstrated query optimization, cost management, and partitioning chops. - Python Data Engineering: Hands-on with Polars or Pandas at scale; deep working knowledge of Parquet partitioning and performance on large joins. - ML Pipelines: Hands-on experience building and deploying components on Kubeflow Pipelines (KFP) and/or Vertex AI Pipelines; working proficiency with Docker. - Event Store Design: Demonstrated experience designing event stores - partitioning by customer, time-ordered event assembly across sources, schema strategy for mixed event types (clicks, purchases, returns). - Communication: Strong written and verbal communication; comfortable being the senior IC voice in design conversations with client stakeholders. Nice to Have - Domain experience in Retail or E-commerce - customer journey data, transaction analytics, returns and exchanges modeling. - Exposure to schema registry tooling (e.g., Confluent), Iceberg, or Delta Lake. - Experience working in client-facing or consulting engagements. - Google Cloud certifications (Professional Data Engineer or equivalent). Work Arrangement & Eligibility - This role requires 3-4 days per week onsite in Seattle, WA. Fully remote and out-of-state candidates will not be considered. - EXL is open to sponsoring H1B transfers for qualified candidates. What You''ll Do - Design and operate event-driven data pipelines using Kafka consumers and Flink jobs to process high-volume customer events (clicks, purchases, returns) in near-real time. - Build and optimize large-scale data transformations on Google Cloud Platform - BigQuery SQL, query performance tuning, and partitioning strategy at scale. - Develop Python data engineering workloads using Polars or Pandas at scale, with rigorous attention to Parquet partitioning, join performance on large datasets, and memory efficiency. - Build, deploy, and maintain ML pipeline components on Kubeflow Pipelines (KFP) and Vertex AI; package and deploy services with Docker. - Design event store architecture: partitioning by customer, time-ordered event assembly across heterogeneous sources, and schema management for mixed event types. - Partner with ML engineers, platform engineers, and data scientists to deliver clean, performant, model-ready data products. - Document architecture decisions and contribute to engineering standards across the platform team. What You''ll Do - Design and operate event-driven data pipelines using Kafka consumers and Flink jobs to process high-volume customer events (clicks, purchases, returns) in near-real time. - Build and optimize large-scale data transformations on Google Cloud Platform - BigQuery SQL, query performance tuning, and partitioning strategy at scale. - Develop Python data engineering workloads using Polars or Pandas at scale, with rigorous attention to Parquet partitioning, join performance on large datasets, and memory efficiency. - Build, deploy, and maintain ML pipeline components on Kubeflow Pipelines (KFP) and Vertex AI; package and deploy services with Docker. - Design event store architecture: partitioning by customer, time-ordered event assembly across heterogeneous sources, and schema management for mixed event types. - Partner with ML engineers, platform engineers, and data scientists to deliver clean, performant, model-ready data products. - Document architecture decisions and contribute to engineering standards across the platform team.