Alchemy Senior Data Engineer at Alchemy builds and operates internal data platform pipelines for product usage, revenue, and cost analysis. The role includes managing ingestion, schema changes, data quality, and collaboration with analytics teams.
Responsibilities
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.
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.
What We Are Looking For
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.
Qualification
Understanding of data modeling
Required
Experience independently building and operating production data pipelines and infrastructure
Strong SQL and Python skills including testing, debugging, and maintainable software design
Experience with Snowflake or comparable warehouse including performance tuning and access management
Experience with workflow orchestration and transformation tools such as Airflow and dbt
Understanding of data modeling, incremental processing, schema evolution, and data-quality testing
Ability to trace data discrepancies from downstream reports to source and fix causes
