Data Engineering Manager at LILT

Job description

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 linkedin.com, Canva linkedin.com, the United States Department of Defense linkedin.com, the United States Air Force linkedin.com, ASICS linkedin.com, 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 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. 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. 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. OUR STORY Our founders, Spence and John met at Google working on Google Translate. As researchers at Stanford and Berkeley, they both worked on language technology to make information accessible to everyone. While together at Google, they were amazed to learn that Google Translate wasn’t used for enterprise products and services inside the company.The quality just wasn’t there. So they set out to build something better. LILT was born. LILT has been a machine learning company since its founding in 2015. At the time, machine translation didn’t meet the quality standard for enterprise translations, so LILT assembled a cutting-edge research team tasked with closing that gap. While meeting customer demand for translation services, LILT has prioritized investments in Large Language Models, human-in-the-loop systems, and now agentic AI. With AI innovation accelerating and enterprise demand growing, the next phase of LILT’s journey is just beginning. OUR TECH What sets our platform apart: - Brand-aware AI that learns your voice, tone, and terminology to ensure every translation is accurate and consistent - Agentic AI workflows that automate the entire translation process from content ingestion to quality review to publishing - 100+ native integrations with systems like Adobe Experience Manager, Webflow, Salesforce, GitHub, and Google Drive to simplify content translation - Human-in-the-loop reviews via our global network of professional linguists, for high-impact content that requires expert review LILT IN THE NEWS - Featured in The Software Report’s thesoftwarereport.com Top 100 Software Companies! - LILT makes it onto the Inc. 5000 List labs.lilt.com. - LILT’s continues to be an intellectual powerhouse, holding numerous patents patents.justia.com that help power the most efficient and sophisticated AI and language models in the industry. - Check out all our news on our website lilt.com. Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to LILT's Privacy Policy at lilt.com. At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at recruiting@lilt.com. LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.

Requirements

Must-haves

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.
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
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.
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.
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.
Brand-aware AI that learns your voice, tone, and terminology to ensure every translation is accurate and consistent

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmsqlpythongokubernetesapimachine learning

Platform: ashby
Effort: quick

Benefits

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