Top Data Engineer Jobs

As a Data Engineer, you will build and optimize data pipelines, manage data storage systems, and collaborate with teams to deliver high-quality data solutions.

The Senior Data Engineer designs and maintains data pipelines, ensures data quality, and supports advanced analytics for U.S. Naval Aviation. They oversee data engineering practices and contribute to enterprise-scale architecture initiatives.

The Data Engineer will build and maintain data pipelines, ensuring high-quality and scalable data systems while working on analytics and machine learning accessibility.

Build and operate scalable data pipelines and analytics-ready datasets using Python, dbt, and BigQuery. Develop Looker dashboards, ensure data quality and observability, optimize performance and cost, integrate data workflows with backend services and CI/CD, and support analytics, experimentation, and production use cases.

Lead and mentor multiple Databricks-focused data engineering teams, define engineering best practices and reusable frameworks, drive CI/CD and performance/cost optimization, build and support analytical and streaming pipelines, and collaborate with architects, product owners, and cross-functional partners to deliver high-quality data products.

The Data Engineer will build reporting solutions, integrate data sources, streamline processes, and ensure data integrity through collaboration with various teams.

As a Senior Data Engineer, you'll design and maintain data applications, develop ETL workflows, and collaborate with teams to harness data for critical missions.

Lead Data Engineer responsible for designing, building, testing, and supporting cloud-based data platforms and streaming solutions. Collaborate across Agile teams, mentor engineers, apply big data and distributed computing tools, and deliver performant, production-grade data services that support product and ML use cases.

The Data Analytics Engineer will develop data models, perform data wrangling, and create dashboards to drive insights for business decisions at Bose Corporation.

Design, build, and operate Spark-based ETL pipelines on AWS. Collaborate with stakeholders to deliver reliable data products, ensure data quality and governance, optimize performance, and maintain operational excellence of the data platform.

Join the Artist-First AI Music lab to build and maintain large-scale distributed data pipelines (Scio/Dataflow), improve data quality via CI/CD, collaborate with cross-functional teams, and support generative-music products using BigQuery and other GCP tooling.

Support design and maintenance of data pipelines, build analytical datasets and feature pipelines, assist with ML model development (LTV, churn, segmentation), integrate APIs, ensure data quality and reproducibility, and partner with cross-functional teams to turn data into actionable insights.

The Azure Data Engineer will work with Azure Cost Management, advising on billing strategies, and managing large datasets for FinOps and analytics.

Build and maintain data quality scripts and automated validation workflows, support large data migrations to modern platforms including public cloud, triage and repair data quality issues, prepare datasets for client-facing materials, and ensure access to databases for business intelligence tools.

Design, build, and maintain scalable data pipelines integrating diverse sources into analytics platforms. Own projects, standardize pipeline tooling, implement automated data tests, ensure security/PII handling, support on-call troubleshooting, and mentor team members to enable reliable, production-grade data delivery.

This co-op position involves developing data products, design and testing of data pipelines, and delivering insights from complex datasets for business decisions.

This co-op position involves developing data products and solving complex data problems using cloud technologies, SQL, and programming languages while gaining mentorship and business exposure.

Design, build, and optimize distributed query solutions using Starburst/Trino. Enable secure, governed data access across cloud and on-premises sources, implement data federation and query optimization, support IAM and governance in a federal environment, develop backend services and CI/CD, and troubleshoot performance in cloud-native systems.

Lead design and delivery of complex, scalable data solutions and pipelines using SQL, Python, Synapse, Snowflake, dbt, and Fivetran. Ensure data quality, security, observability, and compliance (GDPR/HIPAA), mentor engineers, conduct architecture reviews, drive automation and cost/performance optimization, and partner with cross-functional teams to implement governance and data lineage.

As a Data Engineer, you will design scalable data systems, implement ETL operations, and develop pipelines for transformative data insights on mission-driven projects.

Design and build scalable data solutions, develop real-time data pipelines, maintain ETL processes, and promote effective development methodologies.

Senior individual contributor leading design and build-out of enterprise Azure cloud data platform capabilities. Defines architecture, reusable patterns, and standards; provides hands-on implementation, technical mentorship, and cross-functional alignment to enable scalable, secure analytics and data solutions.

Lead design and implementation of enterprise Azure cloud data platform capabilities. Define reference architectures, reusable platform components, standards, and enablement for ingestion, storage, processing, security, and observability. Provide hands‑on engineering, technical leadership, cross‑functional collaboration, and mentorship to ensure scalable, secure, and consistent data solutions.

Design, develop, and monitor large-scale cloud-native data solutions and ETL pipelines; support ML model deployment, ensure code hygiene and CI/CD, mentor engineers, produce documentation and runbooks, and engage with clients to deliver analytics-driven solutions.

Own operational support and optimization of enterprise data warehouse systems on AWS. Build and maintain scalable batch and streaming ETL/ELT pipelines, lead incident management and RCA, implement CI/CD and IaC, collaborate with cross-functional Agile teams, and provide shift-based on-call support while evaluating Generative AI integration opportunities.