Founding Analytics Engineer

Setting up a 401(k) shouldn't be the hardest thing a business ever does. But retirement plan administration is complex, fragmented, and manual — and it slows down both business owners and the advisors who serve them. Aboon was founded to change that.

We're an AI-native third-party administrator (TPA) that makes it easy for advisors to offer 401(k) s and other retirement products by pairing streamlined workflows with dedicated human service. Backed by leading investors including Bain Capital and Edward Jones Ventures, we bring decades of experience building for advisors and the retirement industry. We're looking for exceptional, mission-driven people to build it with us.

We're seeking a senior analytics engineer to own the data layer of our AI-native retirement servicing platform. You'll be our first dedicated data hire, with end-to-end ownership of the transformation, semantic, and AI-access layers.

You’ll be responsible for building and maintaining the models and metrics that answer our most important questions across our product funnel, revenue, servicing operations, and unit economics of AI-delivered plan administration. Just as important, you’ll help us architect the access patterns and semantic layer that enable both our humans and AI agents to leverage our data in our day-to-day operation.

We use managed ETL (Estuary and Fivetran) and zero-ETL for ingestion, so you won't be building bespoke pipelines. Your job is everything downstream: modeling, semantics, governance, and access. You'll join a small, tight-knit team in a flat hierarchy. Our product teams run a Shape Up-inspired cadence; the data function operates alongside it, and you'll serve as the technical SME and go-to resource for all things data across those teams.

Your work will be the shared source of truth for engineering, servicing, GTM, and leadership.

• Own our dbt repo, help us turn legacy queries into models, and build out the marts that power tracking and reporting of our company metrics.

• Build and govern the semantic layer and MCP connectors that let internal teams and AI agents query the warehouse conversationally and safely.

• Coordinate our data dictionary and drive org-wide data access: definitions, lineage, and self-serve analytics so every team can answer their own questions.

• Maintain and extend our product analytics taxonomy and dashboards, keeping event data trustworthy across every product line.

• Help us model the entire business: lifecycle speed, funnel conversion, revenue, and the unit economics of AI-delivered plan administration.

• Partner with engineering on warehouse architecture and data contracts.

• Be the person the company asks first when a number matters.

Our stack

• Redshift + dbt + Metabase.

• Post Hog for product analytics.

• Langfuse for agent traces and evals.

• Asana, Stripe, Hub Spot, Customer.io as source systems.

• Rails/PostgreSQL, zero-ETL to warehouse.

• Estuary/Fivetran for ETL

Required

• 5+ years in analytics/data engineering or an engineering/analyst hybrid role.

• An expert with dbt or similar data-modeling infrastructure.

• Proficient maintaining and working within a BI tool (we use Metabase).

• Proficient working with coding agents; we’re not hand-rolling SQL or dbt models.

• Experience developing a semantic layer (dbt Semantic Layer, Cube, or similar).

• Experience making data accessible to AI — MCP servers, LLM data access, or other similar patterns.

• Skilled and familiar working within product analytics tooling (Post Hog, Amplitude, or similar).

• Working understanding of how a cloud data warehouse operates (we use Redshift).

• Excellent communication skills and comfortable working with non-technical stakeholders directly.

• An instinct for governance and trust — clear metric…