Senior Analytics Engineer

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About Us

The Honest Company (NASDAQ: HNST) is a personal care company dedicated to creating cleanly-formulated and sustainably-designed products spanning categories across diapers, wipes, baby personal care, beauty, apparel, household care and wellness. Launched in 2012, the Company is on a mission to challenge ingredients, ideals, and industries through the power of the Honest brand, the Honest team, and the Honest Standard. For more information about the Honest Standard and the Company, please visit www.honest.com.

Our Mission

We're on a mission to empower people to live happy, healthy lives. We're a wellness brand with values rooted in consciousness, community, transparency, and design. Every day and in every way, we hold ourselves to an Honest standard. We believe that it is our responsibility to do our part to help create a healthy and sustainable future for all.

The Role

The Honest Company is seeking a Senior Analytics Engineer. Our centralized data function runs on Redshift, Airflow, dbt, Metabase, and Hex, and the curated layer is now the expected foundation for analytics. As a Senior Analytics Engineer, you own a full domain of that layer, such as supply chain or syndicated consumption data, from source contract to certified mart.

You are the person stakeholders in that domain go to when a number is disputed. You frame ambiguous requests before anyone writes SQL, decide how the domain should be modeled, and raise the craft of the analysts and engineers working around you.

What you'll do

• Own one or more domains end to end: ERP orders, shipments, inventory, and cost, or Circana syndicated data, including the cross-domain joins behind SIOP and forecast-accuracy reporting.
• Write design docs for domain-scale changes, such as a new conformed dimension or an incremental rebuild strategy, and get them reviewed before building.
• Run definition sessions with supply chain planners and finance analysts; document the agreed definition and bring unresolved conflicts to the Data Council with a recommendation.
• Define dataset tiers and SLAs for your domain, and lead incident response and post-incident review when those datasets break.
• Own runtime and Redshift cost for your domain's models; find and fix the expensive ones.
• Agree schema and quality expectations with Data Engineering for your domain's sources, and hold the line when upstream changes would break them.
• Shape your domain's backlog with the Sr Manager; say no to work with clear reasons and an alternative.
• Mentor analytics engineers and analysts; use code review to teach the reasoning behind standards, not only the standards.
• Lead the migration of your domain's reporting off spreadsheets and raw tables onto curated models.
• Run interview rounds and help calibrate candidates.

What we're looking for

• Required
• 6+ years in analytics engineering, data engineering, or a heavily technical analyst role, including at least 2 years owning models that other teams depended on in production.
• Everything in the Analytics Engineer profile, at a level where others copy your patterns: expert SQL on a columnar warehouse, production dbt (tests, macros, snapshots, exposures, CI), and dimensional modeling across multiple sources.
• A track record of designing, not just implementing: you can show a modeling decision you made, the alternatives you rejected, and why.
• Experience resolving a metric definition that two teams disagreed on, and making the resolution stick.
• Experience owning reliability for datasets with real consumers: tests, freshness, alerting, and incident follow-through.
• Clear technical writing: design docs, PR descriptions, and documentation a business user can read.
• Evidence of making others better: mentorship, review culture, or standards adoption you drove.
• Preferred
• CPG, retail, or consumer goods experience, especially syndicated measurement data (Circana/IRI, NIQ, SPINS) or retailer vendor portals.
• Working knowledge of the SIOP/S&OP cycle or the finance close calendar, and how data timing affects them.
• Experience with a semantic layer, metric store, or data observability platform (Monte Carlo, Datafold).
• Python; exposure to Airflow, Fivetran, or dlt.

What success looks like at 12 months

Your domain's reporting runs on curated models, and its spreadsheets of record are retired.

Your domain's critical datasets meet their SLAs, and incidents end with a fix to the cause.

Stakeholders in your domain bring definition questions to you before they build their own version.

At least one other engineer or analyst ships work in your domain to standard without your review.