Data Engineering Manager

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.

• Own and manage LILT's data transformation layer, ensuring consistency and accuracy in metric definitions across teams.
• Lead a new Data sub-team in Platform Engineering, overseeing the hiring and development of team members while actively contributing to technical work.
• Establish and implement best practices for data engineering, including the use of AI agents in building, testing, and reviewing pipelines and models.

• 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