Data Engineer

Role Overview

As a Data Engineer at Whatnot, you’ll build and scale data systems that power data-driven decisions across the company. You’ll partner with stakeholders across product, sales, marketing, finance, and trust to design reliable data architectures, ship resilient pipelines, and deliver foundational data products for internal and external growth.

Responsibilities

• Own data architecture end-to-end: define how data is captured, modeled, and served; implement in production.
• Make architectural decisions around storage formats, compute patterns, and SLAs balancing cost, scalability, and consistency.
• Build and operate streaming + batch pipelines for high-volume events (e.g., user activity, transactions, experimentation, marketing performance, and telemetry), with strong guarantees for latency, completeness, and accuracy.
• Design canonical, domain-oriented data models that act as the source of truth for analytics, ML, and real-time applications.
• Establish modeling standards, ownership boundaries, and data contracts across teams.
• Enforce data quality at scale using tests, lineage, monitoring, and reconciliation so anomalies are observable and actionable.
• Automate operational workflows by reducing manual data handoffs and reconciling data across services, warehouses, and external systems.
• Enable insights and experimentation by providing high-quality, low-latency data through semantic layers, APIs, and real-time query systems.
• Work across multiple teams (product, analytics, or data platforms) depending on need and experience.

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 dimensional, Data Vault, or ledger-style techniques for analytical and transactional workloads.
• Hands-on expertise across:
  • Ingestion: Kafka, Debezium
  • Transformation: dbt, Spark, Flink
  • Orchestration: Dagster, Airflow
  • Observability: Monte Carlo, Great Expectations
• Experience operating cloud data warehouses such as Snowflake, BigQuery, or Redshift, including schema design, cost optimization, and workload tuning.