Data Engineer

LLR Partners is hiring a Data Engineer to build the data foundation that lets every team at the firm — investment, investor relations, operations, finance and the value creation team — answer the question, today. This is the first dedicated data engineering role at LLR, reporting to our Head of Data. It is a junior-to-mid seat with senior-level ownership: you will build production pipelines, resolve every deal, company and LP to a single record, and consolidate the 40+ systems that power the firm into one governed source of truth.

We are building a modern data platform — a Databricks lakehouse, dbt, Prefect and a proper semantic layer — with machine learning models already running in production on top of it. You inherit real systems on day one, and your work becomes the foundation every future dashboard and AI agent runs on.

Accountabilities

• Ship production data assets. Build and own dbt models, pipelines and dashboards that investment, investor relations, operations, finance and value creation teams use every day.
• Build investment analytics pipelines. dbt models on top of Salesforce, PitchBook and market data sources (SourceScrub, Grata) that turn raw deal activity into trusted metrics — pipeline velocity, conversion, source mix — on demand.
• Power fund and portfolio reporting. Curate data from Allvue, Chronograph and other portfolio systems into a canonical reporting layer — fund NAV, portfolio KPIs and value creation in one place.
• Own entity resolution and master data. One record per company, per LP, per deal — deduplication, matching and golden records across Salesforce, portfolio systems and market data — and help build the semantic layer on top.
• Support machine learning in production. Maintain the feature pipelines, data contracts and monitoring behind the ML models already running in production.
• Bring rigor. Stand up tests, lineage, monitoring and alerting so data quality is measured, not hoped for.
• Partner across the firm. Sit with deal teams, investor relations, operations, finance and the value creation team to understand what they need — then ship for them. Pair with our analytics and ML lead, our infrastructure lead, and the incoming AI Engineer on agentic workflows that consume your data.

Skills and Requirements

• 2–4 years building production data pipelines or analytics models in a real environment — not just personal projects or tutorials.
• Strong SQL and Python — comfortable refactoring messy queries and writing clean, testable pipeline code.
• Hands-on experience with dbt in production — models, tests, docs, and the muscle memory to refactor when needed.
• Experience with a modern cloud data platform — Databricks strongly preferred; Snowflake or BigQuery also considered.
• Comfort with pipeline orchestration (Prefect, Airflow or Dagster) — you can debug a failed run without panicking.
• Curiosity about the business — you want to understand what the data means, not just move it from A to B.
• Strong written communication, clear documentation, and ego in check — you ask before you assume.

Nice to Have

• Experience inside private equity, financial services, consulting or another regulated, document-heavy environment.
• Experience in shared GitHub repositories — branches, pull requests, code review, CI — and software engineering best practices.
• Dashboard experience in Hex, Power BI or Tableau, bringing non-technical stakeholders along.
• Comfort in an Azure environment; exposure to modern deployment platforms and to Snowflake or MotherDuck alongside the Databricks core.
• Familiarity with PE-stack data (Allvue, Chronograph, PitchBook, SourceScrub, Grata) or other fund-administration and portfolio systems.
• Curiosity about how data flows into LLM agents and AI workflows — that's where this stack is headed.