Top Data Engineer Jobs

Design, build, and optimize data pipelines and architecture; extend ML platform; implement ETL using SQL, Python, and AWS; build analytics tools; supervise and mentor junior data engineers; collaborate with development and analytics teams to support data-driven products.

Design, build, and maintain production-grade ETL/ELT pipelines and analytics-ready data products on Databricks and cloud platforms. Collaborate with data modelers, governance, and cross-functional teams to implement dimensional models, optimize pipeline performance, ensure data quality and support BI consumption, while following Agile/SAFe practices.

Support development, testing, and maintenance of ETL/ELT pipelines on Databricks and Azure. Ingest, transform, validate data, assist with dimensional modeling, monitoring, troubleshooting, and documentation. Participate in code reviews, deployments, Agile ceremonies, and collaborate with business and technology partners to prepare data for analytics and reporting.

Design, build, and operate scalable data platforms and ETL/ELT pipelines using Python, SQL, Airflow, and AWS. Optimize data warehouses, ensure data quality and governance, implement CI/CD and infrastructure-as-code, and collaborate with cross-functional teams while mentoring engineers.

The Data Engineer will design and build scalable data systems, contribute to the architecture of the data platform, and collaborate with AI/ML engineers to enhance automation and intelligence in Canoe's systems.

Develop, curate, and publish authoritative geospatial data products in ArcGIS Portal; build Python data-processing pipelines; and create Tableau dashboards and web maps. Manage metadata, hosted feature layers, and data discoverability while partnering with mission users to transform large spatial and tabular datasets into operational visualizations and automated workflows.

Design, build, and maintain data architectures and pipelines to ensure data quality, lineage, and accessibility. Implement ingestion frameworks and transformations across cloud and on-prem platforms, enable data consumption (APIs, views, extracts), monitor data observability, and collaborate cross-functionally to meet privacy and business requirements. Work may include nights/weekends and requires independent technical judgment.

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.

The Principal Data Engineer at NBCUniversal leads the development of data applications, manages data pipelines, and collaborates with teams while coaching junior engineers. Responsibilities include designing scalable data solutions and integrating AI technologies.

The Data Engineer II supports data infrastructure operations, resolves issues, ensures platform stability, and implements improvements while collaborating with engineering and analytics teams.

Design, build, and optimize SQL and NoSQL database solutions (PostgreSQL, Elasticsearch, DynamoDB). Develop stored procedures, functions, triggers, and complex queries. Implement CDC and AWS DMS, manage platform via GitHub/CI-CD and Terraform, monitor and tune performance, participate in Level 3 on-call, and collaborate with analysts, architects, and developers to deliver scalable data services.

The Data Engineer II will build and maintain data pipelines and warehouses, ensure data quality, and collaborate with cross-functional teams to support business intelligence and machine learning efforts.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

The Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

The Data Engineer will architect and develop robust data frameworks for data platforms, focusing on data transformations and automated workflows. Responsibilities include collaborating with stakeholders, ensuring data quality, and maintaining high engineering standards.

The Senior Data Engineer will design and execute data management strategies, build cloud solutions, develop data pipelines, and ensure data quality and integrity.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.

Design, build, and maintain end-to-end data pipelines and integrations using Azure Data Factory, DBT, and Snowflake. Optimize SQL and Snowflake performance, support batch ingestion and API-based integrations, mentor engineers, produce technical documentation and runbooks, and ensure data quality, monitoring, and secure compliant data transfers.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

The Principal Data Engineer will lead the technical strategy for data initiatives, focusing on real-time data platforms and collaboration with stakeholders to design efficient and innovative data architectures, ensuring data governance and compliance.

Build and maintain high-performance ETL/ELT pipelines and data schemas to connect engineering and operations data. Develop Protobuf models and gRPC microservices, administer and optimize Postgres databases, deploy data tools in Docker/Kubernetes, and implement automated data quality tests to ensure reliable, production-ready data for users.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Design, develop, test, implement, and support cloud-based data solutions and pipelines. Collaborate across Agile teams on distributed systems, microservices, ML integration, streaming, NoSQL/RDBMS, and data warehousing (Redshift/Snowflake). Mentor engineers, perform unit tests and code reviews, and optimize performance.

Lead development and delivery of data engineering solutions for clients: design data architectures, build and maintain ETL pipelines, apply advanced analytics, implement BI visualizations, coach teams, manage managed-services SLAs, and engage stakeholders to validate and refine outcomes.