Data Engineer

Role overview

As a Data Engineer at Whatnot, you will build and scale the data systems that power data-driven decisions across the company. You’ll work closely with cross-functional stakeholders (product, sales, marketing, finance, and trust) to design reliable data architectures, ship resilient pipelines, and deliver foundational data products.

Responsibilities

• Own data architecture end-to-end: define how critical business data is captured, modeled, and served in production.
• Make architectural decisions on storage formats, compute patterns, and SLAs balancing cost, scalability, and consistency.
• Build and operate mission-critical streaming and batch pipelines for high-volume, multi-domain event data (e.g., user activity, transactions, experimentation, marketing performance, telemetry), with strong guarantees for latency, completeness, and accuracy.
• Design canonical, domain-oriented data models 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 with tests, lineage, monitoring, and reconciliation so anomalies are actionable.
• Automate workflows by partnering with platform and business systems teams to eliminate manual handoffs and reconcile data across services, warehouses, and external systems.
• Enable insights and experimentation by exposing high-quality, low-latency data via semantic layers, APIs, and real-time query systems.

Requirements

• 3+ years experience as a data or software engineer building data warehouses, distributed data systems, or event-driven architectures.
• Ability to design and implement data models using approaches such as dimensional modeling, Data Vault, or ledger-style techniques supporting analytical and transactional workloads.
• Deep hands-on expertise with modern data tooling across:
  • Ingestion: Kafka, Debezium
  • Transformation: dbt, Spark, Flink
  • Orchestration: Dagster, Airflow
  • Observability/Quality: Monte Carlo, Great Expectations
• Experience operating cloud data warehouses such as Snowflake, BigQuery, or Redshift, including schema design, cost optimization, and workload tuning.

Nice-to-haves / Additional context

• Role may be matched to multiple teams (product, analytics, or data platforms) based on need and experience.