Senior Data Engineer, Alchemy

• Experience independently building and operating production data pipelines and the infrastructure around them
• Strong SQL and Python skills, including testing, debugging, and maintainable software design
• Experience with Snowflake or a comparable warehouse, including performance tuning and access management
• Experience with workflow orchestration and transformation tools such as Airflow and dbt, or equivalent systems
• Understanding of data modeling, incremental processing, schema evolution, and data-quality testing
• The ability to trace a data discrepancy from a downstream report back to its source, explain the cause, and fix it
• Good judgment about stakeholder needs: turn recurring requests into reusable datasets or tooling, and make trade-offs clear

Desirable

• Build and run the internal data platform that helps Alchemy understand product usage, revenue, and the cost of serving customers
• The existing Data Engineer intake centers on ingestion, Snowflake operations, workflow orchestration, data quality, and collaboration with analytics partners
• You will own ingestion, schema changes, and checks that show whether shared datasets are current and correct
• You will work with analytics engineers and data scientists to define those datasets for reporting, analysis, and AI-assisted tools
• Build batch and streaming pipelines that connect operational databases, product events, billing systems, and external sources to the internal data platform
• Make ingestion recoverable through change-data capture, schema-change handling, idempotent processing, and safe backfills
• Operate and improve Snowflake, including access controls, workload management, query performance, and compute and storage costs
• Improve orchestration and deployment workflows so engineers can test, ship, and debug pipelines safely
• Build data-quality checks that detect stale, missing, duplicated, or inconsistent records before they reach downstream consumers
• Work with analytics partners on shared data models, source definitions, and documentation. Make dataset ownership and upstream dependencies clear
• Reduce recurring manual work and build reusable tools for self-service data access
• Participate in production support and incident response for pipelines and the data platform. Restore data flow, reconcile affected datasets, and fix the causes of recurring failures
• Within three months:
• You own a production pipeline and have shipped an improvement to its freshness, reliability, or operating cost
• Downstream users can see whether its data is current, recovery procedures have been tested, and a recurring manual request has become a reusable dataset or tool