Data Engineering Manager

About the role

ABOUT LILT

AI is changing how the world communicates — and LILT is leading that transformation.

We're on a mission to make the world's information accessible to everyone, regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality.

At LILT, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues—Work together, win together; Find a way or make one; Dance in the customer's shoes; Quicker than they expect; Quality is Job 1—guide everything we do. We are trusted by Intel Corporation https://www.linkedin.com/company/intel-corporation/, Canva https://www.linkedin.com/company/canva/, the United States Department of Defense https://www.linkedin.com/company/deptofdefense/, the United States Air Force https://www.linkedin.com/company/united-states-air-force/, ASICS https://www.linkedin.com/company/asics/, and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI.

ABOUT THE ROLE

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

We want to be direct about this role so the right person applies.

- 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