Top Data Engineer Jobs

Lead the architecture and implementation of a new People Analytics platform on a modern data stack, ensuring AI readiness, governance, and security while optimizing for performance.

As a Senior Data Engineer, you will build complex data solutions and pipelines, support analytics growth, and educate users. Collaboration and advanced technical skills are essential.

Design, build, and operationalize scalable data pipelines and transformations on cloud platforms. Ensure data governance, quality, and security; collaborate with data science to support ML model operationalization; mentor engineers and drive architectural decisions for enterprise analytics.

Design, build, and operate production data pipelines across AWS, Databricks, and Snowflake. Modernize and migrate legacy AbInitio workloads, embed data quality/observability/lineage, optimize cost/performance, drive CI/CD and DataOps practices, integrate AI coding tools, mentor engineers, and standardize reusable pipeline frameworks.

Design, build, and maintain cloud-based data infrastructure and ETL/ELT pipelines (Azure, Databricks, Snowflake, Fabric). Lead cloud migrations from on-prem SQL Server, implement Medallion/Lakehouse architectures, ensure data quality and performance tuning, collaborate with analysts and data scientists, and mentor junior staff.

The Lead Data Engineer will architect and develop reporting solutions, integrate AI, analyze complex datasets, and mentor team members to enhance data-driven decision-making.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.

Lead extraction, mapping, cleaning, transforming, and importing complex donor and fundraising data from legacy systems into Virtuous. Use SQL and dbt to design ETL transformations, collaborate on migration strategy, work with customers and internal teams, and solve complex data structure and relationship issues to align legacy data with Virtuous CRM best practices.

As a Senior Data Engineer, you will develop data pipelines, architect innovative data products, and integrate AI/ML models to enhance business insights.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

The Data Engineer will optimize data infrastructure, modernize SQL processes, support data governance, and mentor team members while enhancing analytics capabilities.

Design, build, and optimize scalable data infrastructure and pipelines on AWS using Spark/Scala. Store and model data in appropriate engines and formats, implement knowledge graphs accessible via batch jobs and APIs, enforce automated data quality checks, and collaborate with Data Science and Product teams to deliver performant, cost-effective data services.

Design and implement scalable data infrastructure and pipelines on AWS using Spark and Scala. Build fault-tolerant batch and streaming workflows, optimize storage formats and engines, enforce automated data quality checks, and collaborate with Data Science and Product teams to deliver reliable data assets and APIs.

The Data Platform Engineer will support Corporate Data Science and Analytics teams by curating datasets, optimizing code, and building ETL flows for data products. Responsibilities include managing production services and using cloud technologies.

The Data Engineer will architect and maintain modern data platforms, manage ETL/ELT pipelines, integrate diverse data sources, and champion data governance while supporting advanced analytics and AI workloads.

The Senior Data Engineer designs, builds, and maintains data pipelines and ensures data quality controls while supporting data analytics and reporting needs.

Lead engineering strategy and hands-on implementation for Finance & Actuarial data platforms. Architect no-touch automated data pipelines, drive cloud migrations (Azure Synapse/Databricks/Snowflake), embed AI-assisted development and modern DevOps, establish CI/CD and runbooks, resolve complex incidents, evaluate tooling, and mentor engineering squads while communicating strategy to executive stakeholders.

The Senior Data Engineer will design, develop, and maintain data platforms, create ETL processes, handle data quality issues, and mentor teammates while ensuring scalability and performance in data solutions.

Design and implement scalable data infrastructure in AWS using Spark and Scala. Evolve core data pipelines, store data in optimal engines/formats, build knowledge graphs and APIs, optimize AWS resources, implement automated data quality checks, and collaborate with Data Science and Product teams to deliver reliable, large-scale data services.

Design and implement scalable AWS-based data infrastructure using Spark and Scala. Build fault-tolerant batch and streaming pipelines, optimize storage formats and engines, collaborate with Data Science and Product, implement automated data quality checks, and deliver performant data assets and APIs.

Build and maintain scalable ETL pipelines using Python, Spark, and Airflow; collaborate with AI/ML and Product teams to deliver AI-native data products; identify and resolve ETL bottlenecks; ensure code quality through reviews and tests; own sprint deliverables and contribute to roadmap planning and major epics.

Lead architecture and implementation of an AWS-native data platform powering analytics, ML, and AI workflows. Build scalable ETL/ELT pipelines (Airflow, Fivetran, dbt), enforce governance, observability (Datadog), and data quality, and provide technical leadership across Data Science, Analytics, Product, and Engineering.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

Design and build scalable, fault-tolerant batch and streaming data pipelines and data models for Roku devices and clients. Maintain petabyte-scale data warehouse, ensure data quality, optimize performance, debug large clusters, and collaborate across teams to deliver trusted analytics products and influence roadmap.

Design and build scalable batch and streaming data pipelines and data models for Roku devices and clients. Own data mapping, transformations, quality standards, debugging, and performance tuning on petabyte-scale warehouses. Influence architecture and product roadmap, support existing platforms, and deliver trusted data products enabling analytics and product decisions.