[Hiring] Data Engineering Manager @LILT

Role Description

You own LILT's data transformation layer: the dbt layer and warehouse behind every number LILT reports, from Analytics to our LLM/MCP surface to internal dashboards. Metric definitions are often owned by other teams; you implement and keep them consistent. This layer has no owner today; you make it a role.

You lead a new Data sub-team in Platform Engineering, reporting to the head of Platform, as a hands-on player-manager while hiring and growing a Data Engineer and Senior Data Scientist. You hold decision rights over the transformation layer and warehouse and own their cost.

What You're Walking Into

• You inherit ambiguity.
• No single owner, pipelines to document and rebuild, and no team until your first two hires; until then you write the SQL, dbt, and Python yourself.
• You settle the numbers.
• Finance, Operations, Production, and Product must trust the same metrics; you keep definitions consistent and say no when needed.
• Some things are fixed, most aren't.
• dbt, a single warehouse, and on-prem parity are non-negotiable; warehouse cost is measured and expected to go down. Everything else is yours to decide, with a written case.

The Stack

• Transformation: dbt on BigQuery
• Analytics serving: ClickHouse, Cloud for SaaS, self-hosted on-prem
• Sources: MySQL, replicated to BigQuery
• ETL/orchestration: Python 3, Argo Workflows on Kubernetes
• Consumers: In-app Analytics, Sigma, LILT's Assist agent, LILT's MCP server
• Observability: Datadog
• Agentic engineering: Claude Code and Cursor, used daily across Engineering

Key Responsibilities

• Own the data layer.
• Implement every metric definition once in dbt, consistent everywhere it's used, partnering with the teams that define them.
• 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.

Qualifications

• 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.
• 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.
• 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.

Preferred Skills

• 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.