Top Data Engineer Jobs

Design, build, and maintain scalable data systems and pipelines (batch and streaming); perform complex data modeling and transformations; implement data architectures, governance, and automated deployment pipelines; collaborate with analytics and stakeholder teams to deliver robust, production-ready data products supporting trading and agricultural analytics.

Design, build, and maintain scalable data pipelines and data warehouse solutions on Azure. Perform source data analysis, profiling, ETL development with PySpark, support migrations from on-prem to cloud, troubleshoot production issues, collaborate in Agile teams, and contribute to ML/Generative AI initiatives and Power Apps development.

Build and own the core data models and pipelines connecting product usage, accounts, revenue, billing, and customer health. Create trusted metrics, dashboards, and self-serve reporting. Partner with Product, Engineering, and GTM teams to improve data quality, design AI-enabled workflows and agents, and support leadership with accurate visibility into adoption, pipeline, retention, expansion, and revenue.

Entry-level rotational Data Engineer role focused on SQL-based development, ETL (SSIS), data validation, profiling, performance tuning, and BI reporting. Work with Salesforce/Marketing Cloud integrations, develop and review SQL code, participate in SDLC, client meetings, testing, and cross-team deployments to produce reliable enterprise data solutions.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

The Data Engineer designs and builds AI-driven solutions, optimizes data pipelines, manages integrations, and enhances analytics capabilities across the organization to improve efficiency and decision-making.

The role involves designing and building reliable data pipelines, managing data ingestion from various sources, ensuring data accuracy, and supporting analytics and machine learning efforts.

The Data Engineer will maintain and optimize data pipelines, manage infrastructure, support ML workflows, and collaborate with teams on data products.

The Senior Data Engineer at HealthPartners will build, manage, and optimize data pipelines, collaborate with teams, design technology solutions, and ensure data reliability and quality while mentoring team members.

The Principal Data Engineer at HealthPartners leads data pipeline development, collaborates with teams to enhance data solutions, and implements best practices for data management and integration in healthcare services.

As a Senior Data Engineer, you'll design and maintain complex data applications, build ETL workflows, and lead multi-disciplinary teams in data-driven projects.

As a Data Engineer, you'll develop ETL processes, build data pipelines, manage data catalogs, and collaborate with cross-functional teams on technology solutions.

Design, build, and maintain ETL pipelines and data platforms using Databricks and relational databases. Ensure data availability, support deployments and scalability for AI/ML workflows, collaborate with software engineers and data scientists, and deliver dashboards and analytics for DoD/IC leadership in secure cloud and classified networks.

Design, develop, and optimize Teradata data solutions: elicit requirements, create logical/physical designs, write SQL (views/procs/macros), use Teradata utilities for data loads, tune performance for large datasets, coordinate stakeholders, and write Unix shell scripts.

Elicit and analyze requirements; design logical/physical Teradata solutions; develop ETL using Teradata utilities; write SQL (views, stored procedures, macros, functions); perform performance tuning for large datasets; write Unix shell scripts; coordinate with stakeholders and support BI/DW initiatives.

Design, build, and maintain scalable ETL/ELT pipelines and batch/streaming data platforms. Develop APIs, manage databases, containerize applications (Docker/Kubernetes), implement CI/CD, and lead cross-functional teams to deliver mission-focused analytics solutions. Requires travel and TS/SCI clearance.

Support the Data team by building and maintaining scalable ETL pipelines and data infrastructure for multi-modal medical datasets, automate data QA, optimize data delivery on cloud platforms, and provide analytics tools and secure cross-border data storage.

Design and implement end-to-end data integration and warehousing solutions for large volumes. Implement Hadoop/Hive data movement, script in Linux shell and Python, develop SQL for integration and analysis, and perform high-volume DW performance tuning. Vertica experience is a plus. Requires technical independence and a BS in CS/EE.

Design, architect and manage AWS Big Data solutions; build and tune Hadoop-based ETL/ELT pipelines; process unstructured and real-time data; develop with Java/Python/C++/scripting languages; collaborate on BI/visualization; ensure performance and robust workflows. 6-month contract, US work authorization required.

Build and maintain Azure Data Factory ingestion pipelines into Snowflake, implement medallion transformations (Coalesce), write Python orchestration and connectors, apply governance with Atlan, collaborate with AI coding agents, participate in CI/CD with Azure DevOps, handle on-call production support, and document runbooks.

Design, build, and maintain data transfers and databases. Produce efficient, reusable data engineering code, perform data modeling, UAT, data cleaning, reporting, and technical documentation. Collaborate with stakeholders, participate in code reviews, and improve automation and processes to meet business requirements.

Design, build, and maintain batch and real-time ETL pipelines and data lake architecture using AWS services, Python, PySpark, and SQL. Ensure data quality, governance, and compliance; support reporting and ML models; mentor junior engineers and document data products.

The Data Engineer will develop and maintain data pipelines and data infrastructure for managing large data volumes, ensuring data quality and collaboration with team members.

The Data Engineer will build and optimize data pipelines and infrastructure, ensuring data quality for analytics and machine learning initiatives while collaborating with teams to develop data solutions.

Maintain data integrity and quality through advanced testing and validation of ETL pipelines. Analyze complex data issues, build solutions, mentor junior staff, engage with clients, and support continuous improvement across data management, governance, and pipeline orchestration.