Top Data Engineer Jobs

The Data Engineer II designs, develops, and maintains data integration and analytics solutions, ensuring data quality and accessibility for business stakeholders, while collaborating with teams to meet business needs.

The Data Engineer will design, develop, and implement data solutions, ensuring data integrity and availability, collaborating with stakeholders to meet business analytics needs.

The Data Engineer will design and implement ETL frameworks, perform data analysis, troubleshoot data issues, and optimize data models to meet business needs.

The Data Engineer III builds data infrastructure, manages raw data transformation, maintains data systems, mentors junior engineers, and presents findings to stakeholders.

Join Cox Automotive as an entry-level Data Engineer to build, maintain, and improve cloud-based data pipelines and assets. Work with SQL and Python, use AI-powered coding assistants, support data quality and integrity, participate in Agile development, and gain exposure to Snowflake and AWS AI services.

Lead large-scale migration of document-heavy legacy systems into cloud, convert unstructured documents to analytics-ready data, build MuleSoft integrations and APIs, apply DataWeave transformations, ensure data quality, lineage, governance, and support Salesforce Data 360 implementation and cutover in Agile teams.

The Data Architect will design and optimize data warehouses, analyze business intelligence data, and lead integration efforts to ensure solutions meet quality standards.

Design, build, and maintain data infrastructure and ETL pipelines; automate data ingestion and conversion; extract and analyze data from multiple sources; improve data governance, quality, and metadata; collaborate with analysts, data scientists, and engineers to support ML pipelines and dashboards; communicate project status to leadership.

Build and maintain knowledge-graph solutions: design graph data models, implement and query graph databases, script ETL tasks, work across Linux/Mac/Windows, collaborate with software teams, operate in classified SCIF environments, and communicate system usage to analysts and senior stakeholders.

Design, build, and optimize ETL pipelines and data models for the company data lakehouse. Support migration, Databricks cluster administration, CI/CD with Azure DevOps, data validation, auditing, and performance tuning. Collaborate with analysts and IT to resolve data issues and ensure scalable, accurate data processing.

Design, build, and maintain ETL pipelines and data infrastructure to support analytics and reporting. Write and schedule workflows (Airflow DAGs), manage third-party data ingestion, implement data validation, optimize performance, and collaborate on data models, governance, and ML model deployments.

Support and monitor ETL processes, data lake and reporting systems; troubleshoot and resolve production failures; create reports and processes as needed; communicate with business partners; participate in scheduled on-call rotations and provide status updates to managers.

Design, develop, and maintain data pipelines and software solutions to process and combine diverse data sources. Perform statistical analysis (regression, anomaly detection, clustering), automate and optimize data products, communicate findings to technical and non-technical stakeholders, elicit system requirements, validate performance, and support time-sensitive intelligence operations.

The job involves leading a team to develop data-driven solutions, utilizing various programming languages and cloud technologies, while collaborating with Agile teams and mentoring junior members.

As a Lead Data Engineer, you will collaborate with Agile teams, develop technical solutions, and enhance cloud-based data warehousing to empower financial services for customers.

The Lead Data Engineer will design, develop, and support complex technical solutions using Java, Python, AWS, and big data technologies. Responsibilities include collaborating with Agile teams, mentoring, and delivering cloud-based solutions.

The Senior Data Engineer will build and optimize data pipelines, ensure data integrity, and collaborate with stakeholders to meet business requirements while implementing best coding practices.

The Senior Data Platform Engineer will design, maintain, and enhance Hinge's data platform, including data pipelines, monitoring systems, and user feedback incorporation while ensuring data quality and system reliability.

As a Senior Data Engineer, you will implement data ingestion patterns, build pipelines in Azure, and enhance development with AI tools, aiding the migration to a cloud-based architecture.

Design, build, and maintain scalable ELT/ETL data pipelines using dbt and Prefect; optimize Snowflake performance; implement data quality, security, and observability; collaborate with analysts and data scientists; document data flows and ensure reliable ingestion from external sources.

Design and manage scalable data pipelines and infrastructure, implement ETL/ELT processes, and collaborate with teams to ensure data quality and accessibility.

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 Consultant, Data Engineer will design modern data platforms, build workflows, and translate business needs into technical solutions across various industries.

The role involves designing and optimizing data pipelines in Snowflake, developing ETL workflows, collaborating on data integration, ensuring data security, and performance monitoring.

The role involves building and maintaining financial data pipelines in Databricks, bridging Snowflake, and ensuring data accuracy for finance reporting.