Data Engineering Manager

LILT•Indianapolis, IN

9d•$170,000 - $205,000

About The Position

LILT is seeking a Data Engineering Manager to lead their data transformation layer, including the dbt layer and warehouse that support all reporting, from Analytics to LLM/MCP surfaces and internal dashboards. This role involves implementing and maintaining consistent metric definitions, taking ownership of a critical data infrastructure component that currently lacks a dedicated owner. The position is a hands-on player-manager role within the Platform Engineering team, reporting to the head of Platform. The manager will be responsible for hiring and growing a Data Engineer and a Senior Data Scientist, holding decision rights over the transformation layer and warehouse, and managing their associated costs.

Requirements

7+ years in data/analytics engineering, including 2+ years managing a small team (2-5) with a proven track record of hiring and developing individual contributors.
Fluent in SQL and Python.
Experience building and running production pipelines and a dbt (or equivalent) transformation layer.
Comfortable with BigQuery, ClickHouse, Snowflake, or similar data warehouses.
Experience owning a warehouse or pipeline budget and reducing it with measurable results.
Ability to translate business needs into a technical plan and sequence a backlog against limited headcount.
Experience owning data accountability for finance, operations, and go-to-market stakeholders.
Understanding of B2B SaaS metrics (ARR, ACV, gross margin, on-time delivery) and the impact of definition drift.
Daily use of AI tools, understanding their benefits, limitations, and verification needs.
Ability to document decisions clearly and communicate tradeoffs, risks, and costs crisply to leadership.

Nice To Haves

Experience standing up a data function from zero or reviving an abandoned one.
Experience running dbt in production at scale on BigQuery.
Experience operating ClickHouse.
Experience shipping analytics that run in both cloud and self-hosted environments.

Responsibilities

Own the data layer, implementing every metric definition once in dbt, ensuring consistency across all uses, and partnering with defining teams.
Provide a single point of accountability for Business Operations, Production, Finance, and Product, resolving discrepancies at the definition level.
Run the transformation layer and warehouse, ensuring every pipeline has an owner, tests, and a known cost, with a focus on reducing spend.
Set the strategic direction for the warehouse, ClickHouse's role, and API/MCP exposure, supported by written cases.
Build the Data team by hiring a Data Engineer and a Senior Data Scientist, defining their charter, and managing delivery, quality, and on-call health.
Establish an agentic engineering practice for the Data team, defining how AI agents are used for building, testing, and reviewing pipelines and models, including human review requirements.
Remain hands-on by reading, reviewing, and writing SQL, dbt, and Python code for the team's deliverables.