Data Engineering Manager

LILT is an AI company transforming global communication through machine translation, human-in-the-loop expertise, and language technology. The Data Engineering Manager will own LILT’s data transformation layer and warehouse while leading a new Data sub-team in Platform Engineering. This hands-on player-manager will establish data standards, manage costs and strategy, hire and develop team members, and continue writing and reviewing SQL, dbt, and Python.

Responsibilities:

• Own the data layer. Implement every metric definition once in dbt, consistent everywhere it's used, partnering with the teams that define them. Business Operations, Production, Finance, and Product get one point of accountability; discrepancies resolve at the definition
• Run the transformation layer and warehouse: every pipeline has an owner, tests, and a known cost; spend is measured and goes down
• Set direction: warehouse strategy, ClickHouse's role, and how the layer is exposed via API and MCP, each backed by a written case
• Build the team: hire a Data Engineer and a Senior Data Scientist, set the charter, and run delivery, quality, and on-call health
• Set agentic engineering practice: define how the Data team uses AI agents to build, test, and review pipelines and models, including where human review is required
• Stay hands-on: read, review, and write the SQL, dbt, and Python your team ships

Requirements:

• 7+ years in data/analytics engineering, including 2+ years managing a small team (2-5), with a track record of hiring and developing ICs
• 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
• 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
• 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
• 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
• Run dbt in production at scale on BigQuery; operated ClickHouse
• Shipped analytics that runs in both cloud and self-hosted environments