Permanent
Occasional remote
170K to 205K $ a year
> 5 years
Skills we're looking for
Data engineering
Basic
Clickhouse
BigQuery
DBT
+3
You'll be part of
United States of America
715 employees
Software
Media
Our take
Widely available tools like Google Translate are not fit for complex, operationally intense processes that require expert translations. The manual methods of yesterday can't scale to the volume of digital information produced today. Lilt works with companies, schools, and government agencies to provide AI-powered enterprise language translation. Its platform combines a translation management system and neural machine translation with human language experts to create a “human-in-the-loop” approach. While companies like AI Translate offer intelligent machine translations, they still have linguists fine-tune and correct the texts afterwards. With the Lilt platform, AI and humans work together on text, with the AI making real-time sentence suggestions and the linguist reviewing them immediately. This collaboration delivers 3-5x greater efficiency than traditional human-only translation services, which has aided Lilt's substantial rounds of funding. Clients such as Asics, Canva, and Digital Ocean use Lilt technology for urgent campaigns and large-scale localisation projects. Recently, Lilt launched its V3.5 translation models, which improve quality by 15% to 20%, significantly enhancing translation accuracy and efficiency. Additionally, Lilt appointed a new Chief Revenue Officer to drive growth and scale, underscoring its commitment to expanding its market presence and further improving its services.
You'll be working on
- 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
- 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
- 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
Benefits
- Medical Benefits: Employees receive coverage of medical, dental, and vision insurance, plus FSA/DFSA, HSA, and Commuter benefits. In addition, LILT pays for basic life insurance, short-term disability, and long-term disability
- Monthly lifestyle benefit stipend via the Fringe platform to allow employees to customize benefits to their lifestyle
- Paid parental leave is provided after 6 months
- Compensation: Meaningful equity, 401(k) matching, and flexible time off plus company holidays
We're looking for
- 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