Analytics Engineer

Recurly•Broomfield, CO

2d•$70,000 - $103,000•Remote

About The Position

Recurly's Business Analytics team delivers critical insights to internal and external stakeholders — and we need someone sharp to help us do it better. You'll work with raw data ingested by our data engineering team, building and maintaining the models and views that power ad-hoc analysis and dashboard reporting across the business. This is a grow-from-the-ground-up investment. You'll start by doing great data analysis — clean, well-reasoned, fast — and expand into the full analytics engineering lifecycle over time. It's hands-on and high-visibility: you'll collaborate directly with stakeholders, own your outputs, and be expected to deliver. We're also an AI-forward team. Recurly provides a generous Anthropic token budget, and we expect this person to use it. You should already be comfortable using LLMs to accelerate your workflow — writing better SQL faster, exploring unfamiliar datasets, drafting documentation. Knowing how to prompt well is now part of the job.

Requirements

Innate analytical ability: see patterns others miss, ask the question behind the question, and know when a number doesn't smell right.
Instinct to frame a problem correctly before touching data.
Discipline to sanity-check own conclusions.
Strong SQL skills (CTEs, window functions, aggregations).
Foundational knowledge of dimensional data modeling and data warehouse best practices.
Ability to write and execute data validation tests and resolve what you find.
Customer-facing poise: comfortable collaborating with business stakeholders.
Energy, initiative, and hunger to learn.
Comfortable using AI tools to work faster and smarter.

Nice To Haves

BigQuery, dbt (CLI in a git-based workflow), or Looker / OMNI experience
Python or R for data work
Exposure to payments or subscription data
Familiarity with git and version control

Responsibilities

Build and maintain models and views that power ad-hoc analysis and dashboard reporting.
Perform data analysis, ensuring it is clean, well-reasoned, and fast.
Expand into the full analytics engineering lifecycle over time.
Collaborate directly with stakeholders.
Own outputs and deliver results.
Use AI tools (LLMs) to accelerate workflow, including writing SQL, exploring datasets, and drafting documentation.
Translate between technical and non-technical stakeholders.
Write and execute data validation tests and resolve any issues found.