Staff Data Engineer, Analytics

Whatnot•San Francisco, CA

1d•$207,000 - $290,000•Hybrid

About The Position

Data is crucial to Whatnot’s mission to bring people together through commerce. As our newest Data Engineer, you’ll build and scale the systems that power data-driven decisions across the company. You’ll own the strategy that empowers the Analytics team’s tooling which includes but is not limited to internal reporting, agentic tooling, and experimentation.

Requirements

Have a minimum of 7+ years of experience as a data or software engineer building data warehouses, distributed data systems, or event-driven architectures.
Can design and implement data models using dimensional, Data Vault, or ledger-style techniques that support analytical and transactional workloads.
Have deep hands-on expertise with modern data tooling across ingestion (e.g., Kafka, Debezium), transformation (dbt, Spark, Flink), orchestration (Dagster, Airflow), and observability (Monte Carlo, Great Expectations).
Have operated cloud data warehouses such as Snowflake, BigQuery, or Redshift, including schema design, cost optimization, and workload tuning.
Are comfortable writing production-grade code in Python or SQL languages, and integrating with CI/CD and infrastructure-as-code workflows.
Enjoy partnering across disciplines engineering, product, analytics to translate messy business requirements into elegant data systems.
Thrive as a self-starter in a fast-moving environment, owning both the technical design and the operational outcomes of your work.

Responsibilities

Own the analytics data strategy end-to-end. Define how internal reporting, business intelligence, agentic tooling, and experimentation infrastructure are architected, then drive that strategy into production. You'll make the calls on data models, tooling investments, and delivery patterns that balance speed, accuracy, and long-term scalability.
Drive the AI strategy behind our internal tools. Identify where agentic and LLM-powered tooling can supercharge how the business understands itself, then lead the roadmap that turns those bets into reliable, adopted systems — from prompt and data standards to the infrastructure that makes them trustworthy at scale.
Define and own the domain data strategy. Establish the metrics that matter, the underlying data that powers them, and the standards for how they're modeled, named, and used across the org. Set the contracts and ownership boundaries that keep every team building on the same source of truth.
Guarantee consistency, accuracy, and speed in reporting and intelligence. Build the monitoring, testing, and reconciliation systems that make every dashboard and dataset trustworthy — so decision-makers never have to wonder if the number in front of them is right or current.
Translate raw data into business understanding. Partner cross-functionally with analytics, product, engineering, and business stakeholders to turn complex data into clear, actionable insight — shaping not just what data exists, but how the business actually uses it to make decisions.
Set the technical bar for the data organization. Establish best practices for modeling, tooling, and data quality that other engineers and analysts build on, and mentor the broader org toward higher craft as an IC lead — without a management chain, but with organization-wide influence.

Benefits

Generous Holiday and Time off Policy
Health Insurance options including Medical, Dental, Vision
Work From Home Support
Home office setup allowance
Monthly allowance for cell phone and internet
Care benefits
Monthly allowance for wellness
Annual allowance towards Childcare
Lifetime benefit for family planning, such as adoption or fertility expenses
Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
Monthly allowance to dogfood the app
16 weeks of paid parental leave + one month gradual return to work company leave allowances run concurrently with country leave requirements which take precedence.