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Remote role with broad data requirements attracts many applicants, but seniority reduces volume.
Core data engineering skills (ETL, SQL, Python, cloud) are highly transferable across industries.
Senior ownership and specific stack (Snowflake, dbt, Python) enforce strict technical screening.
Own the complete data platform including ingestion (Fivetran), warehouse (Snowflake), transformation layer (dbt), and customer-facing dashboards (Omni).
Ensure accuracy and reliability of customer-visible reports on activity, completion, and performance with ongoing updates alongside new content.
Drive improvements towards live and near-real-time reporting, monitor pipeline reliability and cost, and integrate data considerations into product design with engineering teams.
Significant professional experience as a Data Engineer with independent ownership of platforms and customer-facing data outputs.
Strong proficiency in Python and SQL, with knowledge of data warehousing (Snowflake), transformation tools (dbt), and BI tools (e.g., Omni, Looker).
Experience deploying data solutions in production with software engineering best practices including testing, code review, and CI/CD.
Work Experience Required: Significant professional experience as a Data Engineer; Notice period: Not explicitly mentioned in the JD.
Senior data engineer comfortable with both backend and data pipeline issues, able to communicate complex technical trade-offs to non-technical audiences.
Experience working remotely with high autonomy, skilled in AI-assisted coding tools and building AI-enhanced data tooling.
Background in building customer-facing data products and collaborating with cross-functional teams (backend, frontend) to embed data within product features.