A complete, annotated resume for a mid-level analytics engineer. Every section is broken down — so you can see exactly what makes a modeling-focused resume stand out to hiring managers.
Scroll down to see the full resume, then read why each section works.
Analytics engineer with 3 years of experience building trusted data models and self-serve analytics infrastructure. Currently at Figma, where I redesigned the dimensional model for product usage data, reducing average analyst query time by 70% and enabling the first company-wide metrics layer. Transitioned from data analyst to analytics engineering with a focus on making data reliable, documented, and accessible without filing a ticket.
Core: SQL (advanced), dbt (models, tests, macros, packages), Snowflake, Looker (LookML) Engineering: Python, Git, GitHub Actions (CI/CD), Airflow Modeling: Dimensional modeling (Kimball), metrics layer design, data governance
Seven things this analytics engineer resume does that most resumes in this space don’t.
Robin doesn’t hide the analyst-to-analytics-engineer transition — the title at Clearbit literally reads “Data Analyst → Analytics Engineer.” But instead of framing it as a career change, the first bullet explains why the transition happened: 60% of analyst time was wasted on data cleaning. Robin identified the problem and built the solution. That’s not a pivot — it’s a promotion earned through initiative.
Robin doesn’t just list “dbt” as a skill. The resume shows what dbt mastery actually looks like: 150+ models, custom macros for SCD Type 2 dimensions, automated testing, auto-generated documentation. This signals to a hiring manager that Robin isn’t running dbt init and following a tutorial — they’re making architectural decisions about how data should be modeled at scale.
The metrics on this resume aren’t about Robin — they’re about the people Robin enables. Query time dropped 70%. Time-to-insight went from 2 days to 3 hours. “Why don’t these numbers match” tickets dropped 90%. These metrics prove that Robin’s work made the entire analytics team more effective, which is exactly the purpose of analytics engineering.
Defining 35 canonical business metrics with standardized definitions isn’t just a technical task — it requires getting buy-in from product, marketing, finance, and leadership on what “DAU” or “activation rate” actually means. Robin’s metrics layer bullet shows both the technical implementation (dbt metrics + Looker) and the organizational impact (eliminating conflicting definitions). This is strategic work, not just SQL.
Most analytics engineers don’t put data governance on their resume because it sounds boring. Robin turns it into a strength: documenting ownership, freshness SLAs, and PII classification for 80+ tables enabled the company to pass its SOC 2 audit with zero findings. This isn’t compliance paperwork — it’s infrastructure that makes the data organization trustworthy.
Setting up GitHub Actions to run dbt build + test on every PR and blocking merges with failing tests is a software engineering practice applied to analytics. This single bullet separates Robin from analytics engineers who push dbt models directly to production without review. It signals that Robin treats the analytics codebase with the same rigor as application code.
Robin doesn’t just say “worked with stakeholders.” The resume names the collaborators: 5 analysts, 2 data engineers, 40+ analysts consuming documentation, the entire GTM organization using the standardized “active customer” definition. Quantifying collaboration proves that Robin can navigate the organizational complexity that comes with defining how an entire company measures itself.
The weak version describes maintenance. The strong version describes a transformation — from what existed before to what Robin built and the measurable impact it created.
The weak version could be anyone with a dbt tutorial on their laptop. The strong version names Figma, a specific redesign, a 70% improvement, and a company-first metrics layer. Unmistakably Robin’s resume.
The weak version dumps every tool into one line and adds soft skills. The strong version categorizes by function, shows depth within each tool (dbt sub-skills, LookML specifically), and includes methodologies like Kimball modeling — which tells a hiring manager Robin speaks the language.
This exact resume template helped our founder land a remote data scientist role — beating 2,000+ other applicants, with zero connections and zero referrals. Just a great resume, tailored to the job.
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