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.

Have 3+ 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.

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
Parental Leave 16 weeks of paid parental leave + one month gradual return to work

Hybrid — New York, Seattle, Los Angeles, San Francisco

Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs.
All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job.

Whatnot is hiring a Data Engineer