Job Brief
Nimble Partners is a venture capital firm focused on the secondary market. We're modernizing our financial technology stack — today, our accounting, investment, and portfolio data lives across spreadsheets, PDFs, email threads, and disconnected systems, and we're building toward a unified data warehouse and reporting infrastructure.
Job Brief
Nimble Partners is a venture capital firm focused on the secondary market. We're modernizing our financial technology stack — today, our accounting, investment, and portfolio data lives across spreadsheets, PDFs, email threads, and disconnected systems, and we're building toward a unified data warehouse and reporting infrastructure.
We're hiring a Data Engineer with strong SQL and Python skills, hands‑on experience building AI/LLM‑powered automations, and enough accounting fluency to validate that financial data and AI outputs are correct — not just technically functional.
You’ll work directly with our accounting and investment teams and our outside technology consultant. As Nimble's first and only technical hire, you'll have real ownership over how our systems and automations are built — a rare opportunity to shape a firm's entire data and AI infrastructure from the ground up, with autonomy to make technical decisions and see them through.
What You’ll Do
Data infrastructure
- Support implementation of a new data warehouse and reporting stack, integrating a new GL, CRM, and portfolio management data.
- Build and maintain data pipelines connecting the GL, CRM (e.g., Affinity), market data (e.g., PitchBook), portfolio tracking, and unstructured documents (quarterly letters, capital account statements, financial statements) into the warehouse.
- Own day‑to‑day administration once live: access, troubleshooting, data quality checks, routine maintenance.
AI automation
- Identify gaps our core software can't automate, and design, build, and deploy AI bots to close them — independently or with our outside technology consultant.
- Provide production support for AI bots: monitor accuracy, debug failures, and refine logic and prompts based on real usage patterns.
- Serve as Nimble's internal owner for AI automation, coordinating with our outside partner on externally‑built bots and building in‑house where it makes more sense.
Data quality & reporting
- Apply accounting knowledge to confirm data and AI outputs are financially correct, not just technically running — build validation, reconciliation, and lineage tracking, and surface uncertainty rather than papering over it.
- Build and maintain reporting dashboards and self‑serve datasets for LPs, accounting, and the investment team.
AI administration & governance
- Administer Nimble's Claude account and other core AI tools — seats, permissions, usage monitoring, cost tracking — and act as the internal point of contact for AI tool questions.
- Help develop Nimble's AI governance and cybersecurity policy alongside Compliance, covering access controls, data classification, and an approved‑tools review workflow.
- Document systems, integrations, and workflows, and collaborate closely with accounting, compliance, investment, and outside technology partners.
What You’ll Bring
- Strong SQL — comfortable querying, tracing, and troubleshooting data across systems.
- Solid Python — for scripting, automations, and API integrations.
- Hands‑on experience building AI bots/agents with LLM APIs (Claude, OpenAI, etc.), including prompt design and basic RAG or tool/function calling.
- Comfort working independently — you'll be Nimble's only technical hire, which means real ownership, fast decision‑making, and the chance to build something from scratch rather than maintain someone else's system.
- Strong communication skills — able to translate technical issues into business terms for finance colleagues and technical detail for outside consultants.
Nice To Have (not Required)
- Familiarity with a modern data stack: ingestion (Fivetran, Airbyte), transformation (dbt), a warehouse (Snowflake, BigQuery, Postgres), orchestration (Airflow, Dagster, Prefect), or a BI tool (Power BI, Looker, Tableau).
- Exposure to finance/fund tech platforms (NetSuite, Sage Intacct, Investran, Allvue, Carta, etc.).
- A portfolio of AI bots/agents you've built (professional or personal projects).
- Document extraction/parsing experience, especially PDF-heavy workflows.
- Exposure to data governance or compliance work in a regulated environment.