Lead Analytics Engineer

Reports to:

Head of Product & Technology

Compensation: $140,000-$160,000

Founded in 2017 by two women of color, PROVEN harnesses groundbreaking artificial intelligence to create personalized, adaptive, clinically‑effective skincare tailored to each individual’s unique needs.

Our belief is simple:
Everyone deserves skincare as unique as they are. Based on our proprietary, award‑winning Skin Genome Project™ database—the world’s largest beauty knowledge base—our technology turns millions of scientific data points into personalized skincare formulations that deliver results.

We’re relentless in our pursuit of science; driven by our love of data; and committed to transforming the one‑size‑fits‑all skincare industry. To date, we’ve earned MIT’s AI Technology of the Year Award and Glossy’s Best Use of Technology Award—not to mention the first‑ever U.S. patent for personalization in skincare. And that’s just the start:
In 2022, we launched our sister company, Noteworthy Scents, to help people discover a fragrance they love through an innovative, AI‑backed fragrance experience.

We have assembled a world‑class team of scientists, engineers, data analysts, creatives, marketers, business experts, customer support specialists, and operations professionals. We’re dedicated to our mission, growing quickly (over 50% just last year), and are always on the lookout for passionate team members. Join us at !

We’re looking for a hands‑on Analytics Engineering Lead who can both build and scale. You’ll start as an individual contributor — designing data models, managing pipelines, building dashboards, providing insights and maintaining data quality across Proven’s modern data stack — and over time grow into a player‑coach
, leading the data function as we scale.

This role is perfect for an analytics generalist who loves getting into the details of SQL, dbt, and BI tools, but also has the strategic thinking to architect data systems and guide how analytics supports the business.

• Build, maintain, and evolve BI dashboards and reporting for leadership, marketing, product, and operations.
• Translate data into actionable insights and clear recommendations that drive growth, retention, and efficiency.
• Conduct deep‑dive analyses into user behavior, funnel performance, campaign effectiveness, and lifecycle metrics.
• Partner with cross‑functional stakeholders to define KPIs and ensure consistent reporting across teams.
• Use Mixpanel and Hex to explore customer journeys and surface trends.
• Support A/B testing analysis, performance tracking, and experiment readouts.
• Own Proven’s data infrastructure across Segment, Stitch, Redshift, Mongo
  
  DB, and dbt
  .
• Maintain and document dbt models for clean, reliable, and reusable data sets.
• Monitor data pipelines and resolve issues related to data quality or schema changes.
• Implement data governance practices to ensure consistency and accuracy across all reporting.
• Partner with Engineering to improve event tracking, data capture, and system performance.

• 5–8 years of experience in analytics engineering, data engineering, or full‑stack analytics roles.
• Strong SQL skills and experience with dbt for data modeling and transformations.
• Proven experience with Redshift (or similar warehouses like Snowflake or Big Query).
• Familiarity with Segment
  , Stitch (or Fivetran), and event‑based data pipelines.
• Hands‑on experience building dashboards and reports in BI tools (Omni, Looker, Tableau, or Mode).
• Experience with Mixpanel or other product analytics tools is a strong plus.
• Python or scripting experience for light automation is a plus.

• Highly detail‑oriented, with a bias for accuracy and accountability.
• Capable of moving between strategic architecture and tactical execution.
• Excellent communication skills — able to explain data concepts clearly to non‑technical stakeholders.
• Strong organizational skills to manage multiple projects and requests simultaneously.

• Eager to mentor and eventually lead a small analytics team.
• Excited by the challenge of building a data culture from the ground up.
• C…