Top Data Engineer Jobs

The Python Data Engineer will enhance data assets, drive requirements for data ingestion and access, and maintain code quality and tools in a dynamic office environment.

Lead a team to design and optimize secure data pipelines and platforms for analytics, ensuring high-quality data for strategic decisions.

The Data Engineer will develop and implement data pipelines, manage ETL processes, and work with big data in a collaborative team environment.

The Data Engineer, Senior will design and maintain data systems, build ETL pipelines, and collaborate with a team to optimize data solutions for client missions.

The Lead Data Engineer will architect and optimize data pipelines, leading a team to create scalable data solutions that support analytics and decision-making.

As a Data Engineer, you'll build data infrastructure for analytics, drive business growth strategies, and enhance personalized user experiences at Eight Sleep.

As a Data Engineer, you'll build data solutions, support analytics, and ensure data quality while collaborating across teams to optimize data management.

As a Data Engineer, you will design and implement scalable data architectures, oversee integration and governance while collaborating on Master Data Management frameworks, to optimize data performance and drive innovation across the organization.

As a Data Engineer, you will design and implement data solutions, automate processes, and support data analytics for the Department of Defense.

Designs, builds, and supports secure data integrations, migrations, and ETL/ELT pipelines across ERP, student information systems, and the data warehouse. Works with stakeholders to gather requirements, automate data workflows, ensure data integrity, monitor integrations, document processes, and partner with security/privacy teams to maintain compliant data access. Supports system implementations and institutional reporting.

Lead design and scaling of enterprise data architecture and pipelines. Build and maintain ETL/ELT and CDC/streaming workflows, improve data quality and observability, own schema and warehouse modeling, implement governance (RBAC), and provide cross-functional technical leadership to enable trusted, scalable data for analytics and product teams.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.

As a Data Engineer at Booz Allen, you'll analyze and visualize structured and unstructured data to derive insights, working closely with stakeholders and leveraging advanced technologies for decision-making support.

The Data Engineer will develop scalable data pipelines, build applications using Palantir Foundry, and collaborate with stakeholders on data integration for decision-making.

The ETL Lead will design and manage scalable data pipelines, optimize data lake architectures, and lead data engineering initiatives in a cloud environment.

Lead design, build, and optimization of the enterprise data warehouse and production data pipelines. Own platform architecture, integrate legacy systems (AS400), support Power BI semantic models (DAX), implement monitoring, CI/CD and governance, and mentor engineers to transition from consultant-run platforms to an internally managed, scalable data ecosystem.

Design, build, and maintain scalable ETL/ELT pipelines and data models to populate the data warehouse. Write reusable SQL and Python to transform data, monitor pipeline reliability and quality, enable AI workflows, collaborate with stakeholders, and drive adoption of modern data stack technologies (DBT, Iceberg, Databricks). Support data governance, observability, and security practices.

Design, own, and scale an enterprise Databricks/Azure data platform supporting analytics and interoperability. Lead integration architecture (APIs, ADF, event-driven), build high-performance Python/SQL pipelines, enforce governance (Unity Catalog), ensure healthcare compliance, mentor engineers, and drive CI/CD and IaC for reliable, secure PHI/PII handling.

Develop, deploy, and support large-scale distributed Big Data applications. Design multi-tier software, participate across the SDLC, perform database design and performance tuning, troubleshoot production issues, and collaborate with internal clients. Work primarily with Hadoop, Spark, Impala, Hive in Unix/Linux and Windows environments within financial/consumer banking contexts.

Develop, deploy, and support large-scale distributed Big Data applications. Design multi-tier software across the full SDLC, perform problem investigation and performance tuning, work in Unix/Windows environments, and collaborate with finance domain teams. Use Hadoop, Spark, Impala, Hive, batch and online processing, shell/Perl scripting, and ETL/job-scheduling tools as needed.

Lead cross-functional analytics initiatives by gathering requirements, prioritizing projects, and guiding Enterprise Data Office pods. Define data requirements, validate analytical outputs, and manage project timelines using Azure DevOps and Power BI. Implement and maintain scalable Azure data pipelines, Snowflake data warehouse enhancements, dbt transformations, SSIS, and integrations. Communicate status to stakeholders and perform post-delivery evaluations to capture lessons learned.

Lead design and implementation of scalable real-time and batch data pipelines for programmatic advertising. Ingest, normalize, transform, and model bid stream, impression, and revenue data; maintain data warehouses and lakes; implement data quality and privacy compliance; support BI teams and optimize yield through analytics and feature engineering.

The Data Engineer will develop end-to-end data pipelines, optimize data architectures, and implement machine learning solutions to enhance business intelligence capabilities.

Provide technical leadership for the MFS data platform, collaborate with cross-functional engineering teams to improve platform adoption, mentor engineers, and design and develop large-scale distributed systems using Big Data technologies and Kafka.

Design, build, and optimize enterprise-scale data pipelines and data models in Databricks to support analytics, AI, and operational systems. Lead MDM initiatives, implement data governance, lineage, and quality frameworks, and collaborate with engineering and business teams to ensure scalable, reliable, and cost-efficient data platforms.