Top Data Engineer Jobs

The Sr. Data Engineer will build and manage data pipelines for large datasets, collaborate with teams, and leverage cloud technologies for data processing.

As a Data Engineer, you will design, build, and manage data pipelines, improve data architecture, ensure accuracy, and collaborate with various teams.

Design, build, and maintain data models, ETL pipelines, and SQL-based solutions to support reporting and analytics. Analyze source systems for data quality, develop data integration using Databricks/Informatica/Pentaho, and produce documentation, dashboards, and BI artifacts (Power BI/OBIEE/Tableau) to support DSNY operations.

Design, implement, and maintain scalable ETL and real-time data pipelines using Apache NiFi; architect and optimize Snowflake data warehouses; administer Linux-based cloud environments (Azure/AWS/GCP); build CI/CD workflows; enforce data governance, lineage, and auditing; troubleshoot platform issues; mentor staff and document best practices to support micromobility and delivery analytics for the Department of Sustainable Delivery.

Design, build, and maintain cloud-based data pipelines and ETL processes to populate data warehouses. Optimize storage and performance, enforce security and data quality, automate tasks with scripting, mentor junior engineers, and evaluate new technologies to improve scalability and reliability.

Design, build, and maintain cloud-based data pipelines and ETL into data warehouses (Snowflake/AWS). Ensure data quality, security, and performance; optimize storage and retrieval; automate tasks with Python/Bash; monitor/troubleshoot pipelines; mentor junior engineers; evaluate new technologies and support legacy systems.

Design and build enterprise-scale Snowflake environments, develop and optimize data pipelines with Python, dbt, and Airflow, lead client technical strategy, implement Snowflake security/compliance, and mentor junior engineers while collaborating with Snowflake, AWS, and Anthropic partners.

Own and improve the company data stack: expand the data warehouse, build and streamline Python-based ingestion and ETL processes, refactor dbt models, maintain data quality, standardize reporting, and collaborate with engineering and analysts. Act as technical owner for parts of the stack and proactively drive improvements and automation.