- Own data architecture end-to-end. Define how we capture, model, and serve critical business data then implement it in production. You’ll make architectural decisions around storage formats, compute patterns, and SLAs that balance cost, scalability, and consistency.
- Build mission-critical pipelines. Develop and operate streaming and batch data workflows that process high-volume events across multiple domains user activity, transactions, experimentation, marketing performance, and operational telemetry with tight guarantees for latency, completeness, and accuracy.
- Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and real-time applications. Establish and enforce modeling standards, ownership boundaries, and data contracts across teams.
- Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
- Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
- Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, low-latency data through semantic layers, APIs, and real-time query systems.
👋 You
People who do well at Whatnot tend to be comfortable figuring things out as they go, biased toward action, and genuinely curious about what they're building. They care more about outcomes than credit and stay close to the product and the people using it.
- 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.
If designing the data backbone that powers Whatnot’s growth, insights, and product innovation excites you, we’d love to meet you.
We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs.
💰Compensation
For Full-Time (Salary)
The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills, and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity.