Founding Analytics Engineer

Ambrook

Founding Analytics Engineer at Ambrook managing data layer and driving data literacy. Leading data function improvements and collaboration across teams in a fast-paced startup environment.

Posted 4/16/2026full-timeNew York City • New York • 🇺🇸 United StatesMid-LevelSenior💰 $140,000 - $210,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowBigQueryCloudETLSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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Hard Skills & Tools

SQLdbtETLdata modelingdata lineagedata auditingdata documentationstatistical modelingAI automationdata literacy

Soft Skills

business instinctscommunicationproblem-solvingadaptabilityteam collaborationeducational skillsanalytical thinkingself-sufficiencytrust-buildingprocess improvement