Data Engineer - Remote

Role Overview

You will be a Data Engineer helping Whatnot build and scale systems that power data-driven decision-making across the organization. The role focuses on owning end-to-end data architecture, developing production data pipelines, and creating foundational data products for analytics, machine learning, and real-time applications.

Responsibilities

• Own end-to-end data architecture: define how critical business data is captured, modeled, and served in production.
• Make key architectural decisions around storage formats, compute patterns, and service level agreements (cost, scalability, consistency).
• Build and operate high-volume streaming and batch workflows, processing events for:
  • user activity
  • transactions
  • experimentation
  • marketing performance
  • operational telemetry (with guarantees for latency, completeness, accuracy)
• Design canonical data models that act as authoritative sources for:
  • analytics
  • machine learning
  • real-time applications
• Establish standards, ownership boundaries, and data contracts to maintain data integrity.
• Ensure data quality and observability at scale via monitoring, lineage, and reconciliation.
• Automate operational workflows by integrating with business systems and platform teams to reduce manual data handoffs and reconcile across services, warehouses, and external systems.
• Enable experimentation and insights with low-latency, high-quality data through semantic layers, APIs, and real-time query systems.

Requirements

• 3+ years experience as a data or software engineer building:
  • data warehouses
  • distributed data systems
  • event-driven architectures
• Ability to design and implement data models using dimensional, Data Vault, or ledger-style techniques.
• Strong hands-on experience with modern data tooling, including:
  • ingestion (e.g., Kafka, Debezium)
  • transformation (e.g., dbt, Spark, Flink)
  • orchestration (e.g., Dagster, Airflow)
  • (observability/monitoring is implied via data quality and lineage responsibilities)

Nice to Have

• Experience building semantic layers, data APIs, and real-time query systems.
• Experience supporting ML and product teams with production-grade, low-latency datasets.