Founding Analytics Engineer

Ambrook helps American family-run businesses become more profitable and resilient.

From volatile markets to climate shifts, independent operators face mounting pressure. While sustainable investments often yield the best long-term returns, they require financial clarity and capital that fragmented legacy systems can’t provide.

We are rebuilding the financial infrastructure that independent operators rely on. By replacing paperwork with modern tools for accounting, banking, and spending, Ambrook gives owners the data they need to prove viability to lenders and the next generation. We empower the stewards of land and labor to make confident investments in their future.

We’re a Series A startup backed by Thrive Capital, Dylan Field, and Homebrew. We’re looking for early team members to help us untangle the intersection of American industry, climate, and the economy.

We're looking for a Founding Analytics Engineer to own Ambrook's entire data function. You'll take an established warehouse and transform it into a clean, well-modeled, trustworthy foundation that enables every team and every agent to pull the right data with confidence.

You'll own the full stack from ingestion to insight and help build the function that will make every team at Ambrook fully data-driven.

We're looking for someone who we can count on to…

Own

• The entire data layer downstream of production databases — warehouse, dbt models, Airbyte pipelines, orchestration — plus primary company-wide dashboards, data access patterns for AI agents, and an advisory role on data modeling in external systems (e.g., HubSpot) to keep upstream data clean.

Teach

• Data literacy across the company — how to think about metrics, write better queries, and self-serve in Hex. Best practices for structuring data in the tools teams own. Metrics definitions and consistency so the team asks the right questions of the data.

Improve

• The data model. Grow our capacity by resolving gotchas, improving documentation, and building trust. Enable teams to self-serve on reliable data.

• Get deep into Ambrook's business model, product, and customers to understand how we make money and who we serve.

• Trace the full data lineage from product databases through Airbyte into the warehouse to understand the plumbing and where things can break.

• Audit the existing warehouse schema, dbt models, and Hex dashboards; document the known gotchas and tribal knowledge.

• Meet with Growth, Ops, CS, Product, and Engineering to understand their data needs and pain points.

• Own the primary company-wide dashboards — have audited, corrected, and taken accountability for the core dashboards the whole company relies on.

• Begin evolving the data model — started refactoring dbt models to address the highest-priority gotchas and quality issues identified in month one.

• Establish a plan for agent data access — define clean naming conventions, clear data access patterns, and a recommendation on what level of data LLMs should be able to query (raw vs. curated semantic layer.)

• Stood up initial systems that enable teams to self-serve with confidence. The team has processes and tools to unblock themselves with occasional intervention.

• Deliver a significantly improved data model with well-documented models and a meaningfully more trustworthy warehouse.

• Have agent data access operational or in progress with a clean semantic layer or access patterns in place so LLMs can pull data reliably.

• Actively educate the team on data concepts through documentation, 1:1s, and workshops so the team becomes more data-literate and self-sufficient.

• Shape the future of the data function — actively evaluate how AI is changing the data stack and make recommendations on how Ambrook's infrastructure should evolve.

• Proven experience as a solo or early data hire. You've owned the full stack and built infrastructure or the function from scratch.

• Advanced SQL and production dbt, with hands-on experience in a cloud warehouse (BigQuery or similar) and ETL tools (Airbyte or comparable.)

• Strong business instincts. You translate ambiguous questions into clean data models and communicate findings clearly to non-technical teammates.

• Comfortable in fast-moving environments with high-volume sales funnels.

• AI-native approach to data work. You think in terms of agent automation (quality checks, column generation, automated reviews) and actively use AI tools to move faster.

• Bonus: Experience with Airbyte, Hex, and/or BigQuery.

• Bonus: Experience with Airflow or Dagster for orchestration.

• Bonus: Basic ML or statistical modeling experience.