Lilt · Posted 12d ago
Data Engineering Manager
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇺🇸 United States
- Contract
- Full-time
- Role
- Data leadership
- Source
- Stapply Data
The posting
Join LILT as a Data Engineering Manager, where you will own the data transformation layer and warehouse, lead a new Data sub-team, and ensure consistency in metric definitions across various teams. You will also have the opportunity to build your team, set direction for the data layer, and stay hands-on with SQL, dbt, and Python. Enjoy benefits such as medical coverage, a monthly lifestyle stipend, paid parental leave, and meaningful equity.
- Ownership of LILT's data transformation layer, including the dbt layer and warehouse, ensuring consistency in metric definitions. - Leading a new Data sub-team in Platform Engineering, hiring and growing a Data Engineer and Senior Data Scientist, and making decisions regarding the transformation layer and warehouse. - Settling discrepancies in metrics across Finance, Operations, Production, and Product, ensuring trust in the numbers reported.
- Cost and roadmap ownership: has owned a warehouse or pipeline budget and reduced it with measurable results; translates business needs into a technical plan and sequences a backlog against limited headcount - People management: 7+ years in data/analytics engineering, including 2+ years managing a small team (2-5), with a track record of hiring and developing ICs - Hands-on fundamentals: fluent in SQL and Python; has built and run production pipelines and a dbt (or equivalent) transformation layer; comfortable with BigQuery, ClickHouse, Snowflake, or similar - Stakeholder and business metrics: has owned data accountability for finance, operations, and go-to-market stakeholders, and understands B2B SaaS metrics (ARR, ACV, gross margin, on-time delivery) and how definition drift breaks them - Effective AI use and communication: uses AI tools daily and knows where they help, mislead, and need verification; documents decisions clearly and communicates tradeoffs, risk, and cost crisply to leadership - Stood up a data function from zero, or revived an abandoned one - Shipped analytics that runs in both cloud and self-hosted environments - Run dbt in production at scale on BigQuery; operated ClickHouse