Data Engineer at Commerce responsible for building data foundations to support AI Operations workflows and advanced analytics. Collaborating across teams to ensure clean and reliable data for business automation and decision-making.
Responsibilities
Design and maintain data lake architectures that support AI Operations workflows, analytics, and automation.
Build and optimize ETL/ELT pipelines to ingest raw data from internal and external tools into Snowflake and other structured data stores.
Partner with Business Analysts to translate business requirements into scalable, production-ready data solutions.
Collaborate with Automation Specialists to connect data pipelines with workflow automation requests, custom GPTs, and AI assistant integrations.
Support Full Stack Engineers by developing and maintaining the MCP Server that connects data services with internal tools and systems.
Work with Business Analysts to validate data quality and ensure accuracy across key datasets and workflows.
Troubleshoot and resolve data ingestion issues, recommending improvements to infrastructure, processes, and tools.
Monitor and maintain all data workflows within the AI Operations ecosystem, ensuring performance, reliability, and scalability.
Requirements
3+ years of experience in data engineering, data warehousing, or related fields
Strong SQL and Python skills
Hands-on experience building ETL/ELT pipelines (Airflow experience a plus)
Skilled in Snowflake or similar cloud data warehouses (BigQuery, Redshift)
Familiar with data lake concepts and best practices
Comfortable working within AWS, GCP, or similar environments
Familiar with Linux command-line operations
Passionate about AI and eager to explore practical ways to enable automation through data
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