Analytics Engineer

About FullEnrich

FullEnrich started with a simple goal: build the best way to find the emails and phone numbers businesses need to reach the right people. Most enrichment tools rely on a single data source, which means patchy coverage and inconsistent quality. We took a different path: FullEnrich aggregates 20+ data providers into a single waterfall system, automatically querying each source until we find the best possible contact info. The result is the highest coverage on the market, with better data quality.

Since then, we've expanded beyond core enrichment into personal email for recruiters, reverse email lookup for inbound lead qualification, list building, and native CRM integrations that keep your source of truth clean and fresh. Today, FullEnrich is profitable, trusted by 3,500+ customers including AWS, Shopify, Deel, and HubSpot.

Why this role, why now

Until now, our data stack has been built and maintained by our CPO and RevOps alongside their primary responsibilities. We’ve built a strong and reliable foundation: a well-structured dbt project on BigQuery with clear conventions and automated testing, robust ETL and reverse ETL pipelines, product tracking flowing into the warehouse from day one, and self-serve dashboards in Basedash.

The stack is already in a very good state, you won’t be fixing chaos or starting from scratch. You’ll be able to build on top of a clean and well-designed foundation from day one.

This role sits between data infrastructure and business operations. You’ll start by owning the data layer end-to-end: making it even more robust, scalable, and actionable for the business. But the goal is not just to maintain the stack, it’s to turn it into a real leverage point for every team at FullEnrich (Sales, CS, Finance, Marketing, Product).

But as the company grows, every team needs more from data than two people can deliver part-time. We need someone who wakes up thinking about data modeling.

This is our first dedicated data hire. You're not joining a data team. You're starting one.

You’ll report directly to Simon (RevOps), who is actively working on the current data stack and will work closely with you from day one to help you prioritize, ramp up quickly, and expand your scope over time.

What You'll Do

You'll own the data stack end-to-end. Your job is to make sure every team at FullEnrich, Finance, Marketing, RevOps, Sales, Support, has reliable access to the metrics they need to make decisions.

Most of the work is dbt on BigQuery: designing models, enforcing quality, writing tests, keeping documentation sharp. The ambition is to have every important metric pre-computed in dbt so it can be consumed in self-service through Basedash (an LLM-native BI tool). New metric needed? That's a new project: understand the requirement, set up the pipeline, model the data, ship the dashboard.

Our philosophy is "buy, don't build." We use dbt Cloud for orchestration, Fivetran and Airbyte for ingestion, Segment for reverse ETL, Basedash for BI. We invest in the best off-the-shelf tools so you can focus on the two things that actually move the needle: defining data models that accurately represent the business, and running the analyses that change the company's trajectory. Not plumbing.

We're also AI-native in how we work. We write our entire dbt project in Cursor (or Claude Code, if you prefer) with AI-assisted development. We're looking for someone who already uses AI to augment their work and sees it as a multiplier, not a gimmick.

BigQuery is becoming the nervous system of the company. It's not just an analytics warehouse; it's the single source of truth for every customer, every prospect, every transaction. Beyond powering dashboards, this data layer will feed future growth operations: outbound, email nurturing, lead scoring, territory planning. The models you build won't just be consumed by analysts; they'll be consumed by the teams and systems that drive revenue.

The data models you build will be around for years. How you choose to represent the business in tables and concepts will shape how every team thinks about their numbers. This is foundational work with lasting impact, not throwaway queries.

But this isn't just a "build dashboards" role. We expect you to go beyond requests and do real analytical work. Here's the kind of questions you'll tackle:

• Provider cost & margin analysis. We route enrichments through 20+ data providers in a waterfall. Each has a different cost and hit rate. Today we don't have granular visibility into margin by provider. You'll build the models that let us understand unit economics per enrichment and inform decisions on waterfall ordering and provider selection.
• Activation scoring. Which user actions predict retention and conversion? Does inviting three teammates lead to better retention? Does connecting HubSpot predict upgrade? You'll model activation events from Segment, cross them with retention and conversion data, and help us understand what drives product-qualified behavior.
• Credit consumption patterns. Our customers buy credits and burn them at different rates. Understanding the relationship between consumption velocity, plan type, and outcomes (upgrade, churn, renewal) is critical for pricing, CS, and forecasting.

You'll also be the person who implements reverse ETL when a team needs operational data somewhere new. Support needs a specific metric in Intercom? You build the pipeline that pushes it there. Marketing needs enrichment data synced back to HubSpot? Same thing.

A typical project looks like this: Marketing launches a webinar series. You set up the ETL from the webinar platform into BigQuery, model the data in dbt alongside existing CRM and marketing data, and deliver a performance dashboard that Marketing can use on their own going forward.

Who you are

Experience & Skills:

• 4+ years of experience in analytics engineering, data engineering, or a senior data analyst role with strong modeling skills.
• You’ve worked on end-to-end analyses for demanding stakeholders : from framing the problem and challenging assumptions, to building the right data models, delivering insights, and iterating based on feedback.
• Strong SQL : you're comfortable in BigQuery or equivalent
• You already use AI tools (Cursor, Claude, or similar) to work faster
• Solid understanding of SaaS metrics: ARR, churn, activation, unit economics
• Builder mindset: you'd rather automate a recurring task than do it twice
• Comfortable with ambiguity, autonomous, and rigorous about data quality

Bonus Points:

• Experience with BigQuery specifically (materialized views, partitioning, clustering).
• Familiarity with Segment as both a data source and reverse ETL tool.
• Experience at a fast-moving startup where priorities shift and you adapt.

How we work togeteher

At FullEnrich, we’re a high-ownership, entrepreneurial team with deep experience across sales, marketing, and tech.

We value:

• Rapid execution
• Clear, business-focused outcomes
• Autonomy and trust — you’ll fully own your scope and execute your vision

We collaborate closely, experiment fast, and stay relentlessly focused on impact. If you’re passionate about scaling smart, building meaningful systems, and having a huge impact early, you’ll love working here.

Hiring process

We keep it fast, transparent, and human:

1. First Call (45 min) with Simon — Quick mutual fit check
2. Technical Case (1h) — A real-world data/ops problem. Show us how you think
3. Final Interview with Founders — Culture and vision alignment