SWE, Data - Notifications

Whatnot•San Francisco, CA

5d•Hybrid

About The Position

Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. We’re re-defining e-commerce by blending community, shopping, and entertainment into a community just for you. As a remote co-located team, we’re inspired by innovation and anchored in our values . With hubs in the US, UK, Germany, Ireland, and Poland, we’re building the future of online marketplaces –together. From fashion, beauty, and electronics to collectibles like trading cards, comic books, and even live plants, our live auctions have something for everyone. And we’re just getting started! As one of the fastest growing marketplaces , we’re looking for bold, forward-thinking problem solvers across all functional areas. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business, and bring people together through commerce. 💻 Role At Whatnot data engineers build foundational data systems that power product development, experimentation, machine learning, and operational decision-making across the company. As an L5 Data Engineer on the Notifications Platform, you will play a critical role in owning and evolving one of the highest-volume and highest-impact data domains at Whatnot. Notifications generate hundreds of millions of events per day, and the underlying data systems support everything from ML iteration and experimentation to adhoc engineering debugging. In this role, you will define the technical direction for the Notifications data mart, harden it to production-grade reliability, and set new patterns for domain-owned data engineering. Working in a highly cross-functional role, you will collaborate closely with Product Engineers, Data Scientists, ML Engineers, and the Analytics Platform team. You’ll make key architectural decisions around data modeling, reliability, latency, and cost — and then make them real.

Requirements

5+ years of experience in the data or software engineering domain
Strong experience building and maintaining production-grade data pipelines with clear SLAs, monitoring, and alerting
Deep expertise in SQL, including complex model graphs, dependency management, and performance optimization
Are comfortable writing production-grade code in Python or SQL languages, and integrating with CI/CD and infrastructure-as-code workflows.
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.
Proven track record working with large-scale datasets (hundreds of millions of rows per day)
Experience designing data models that balance analytics, ML, and operational debugging use cases
Strong systems thinking — you consider correctness, latency, cost, and maintainability together
Self-starter ethic, thriving under a high level of autonomy
Exceptional interpersonal and communication skills in cross-functional environments

Responsibilities

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 batch data workflows that process high-volume events related to notifications 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 production 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, self-healing, maintainable data assets.

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
Parental Leave
16 weeks of paid parental leave + one month gradual return to work company leave allowances run concurrently with country leave requirements which take precedence.