> Markdown version of [/jobs/ext/1862267-data-engineer-cx](https://www.wearedevelopers.com/jobs/ext/1862267-data-engineer-cx). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer, CX - **Company:** Whatnot Inc. - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $180,000.0 - $260,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Airflow, BigQuery, Business Systems, Continuous Integration, Data Architecture, Data Infrastructure, Data Systems, Data Vault Modeling, Data Warehousing, Distributed Data Store, Python (Programming Language), SQL Databases, Systems Integration, Large Language Models, Snowflake, Apache Spark, Data Layers, Event Driven Architecture, Debezium, Apache Flink, Data Analytics, Apache Kafka - **Published:** July 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8e1bb5d1e28bb584 ## About the Role 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 5+ 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, operations, 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. ## Description 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 work directly with stakeholders across the business - such as product, customer experience, logistics, finance, and trust teams - to design reliable data architectures, ship resilient pipelines, and create the foundational data products that power Whatnot's internal and external growth. On any given day, you will: * Own data architecture end-to-end. Define how we capture, model, and serve critical business data. You'll make architectural decisions around storage formats, compute patterns, and SLAs that balance cost, scalability, and consistency. * Lead the CX and Logistics Data Domains. Act as the data owner for our Customer Experience (CX) and Logistics domains, including ownership of data foundations and stewardship for several mission-critical metrics and concepts (refunds, customer satisfaction and sentiment, shipping, margin, and more). * Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, LLMs, 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. Experience ingesting and operationalizing third party-created data is a plus. * 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. Stand up platforms to enable novel experimentation approaches within the company. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Python-Based Data Streaming Pipelines Within Minutes](https://www.wearedevelopers.com/videos/1233-python-based-data-streaming-pipelines-within-minutes) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Making Data Warehouses fast. 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