Top Senior Data Engineer Jobs

Lead and mentor multiple Databricks-focused data engineering teams, define engineering best practices and reusable frameworks, drive CI/CD and performance/cost optimization, build and support analytical and streaming pipelines, and collaborate with architects, product owners, and cross-functional partners to deliver high-quality data products.

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.

Design, build, and maintain Scala-based ETL and streaming data pipelines, APIs, and data microservices in Azure. Optimize LLM fine-tuning and RAG workflows, support DevOps/CI-CD practices, document standards, mentor engineers, and manage data ingestion frameworks for analytics and partner integrations.

Design, build, and maintain data pipelines and models for hardware manufacturing, device traceability, yield, and supply chain. Integrate MES/ERP/IoT data into a lakehouse, ensure end-to-end data quality, security, and governance, and partner with Hardware, Operations, and Quality teams. Support lakehouse architecture (Iceberg, Databricks, dbt) and reduce single-point-of-failure risk for manufacturing data.

The Senior Data Engineer will build data pipelines and infrastructure, modernize legacy systems, and ensure data reliability for analytics at ABCorp.

Design, build, and optimize the company lakehouse environment and ETL/ELT pipelines (Databricks/Azure/Fabric). Develop semantic models, governed metrics, data quality controls, monitoring, and CI/CD practices. Partner with business, security, and analytics teams to steward master data, implement governance and access controls, and enable reliable datasets for reporting and AI use cases.

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.