Build and scale the ingestion layer across third-party marketing APIs (Meta, Google, TikTok, GA4, Shopify, Klaviyo, and more) — auth, extraction, rate-limit handling, backfill, and incremental sync.
Design normalization and transformation pipelines that map messy, platform-specific data into shared, queryable schemas (e.g. a unified creative/campaign/order model).
Own data reliability at scale — sync accuracy, freshness, coverage, and observability. Build the systems that detect when a connection breaks or a number looks wrong before a user does.
Engineer for multi-tenant scale and security: pipelines and storage that stay performant and cost-efficient across 1,000+ users and hundreds of connected brands — with strict data isolation, privacy, and compliance built in, not bolted on.
Partner with the AI and data-science teams to expose clean, well-modeled data the agent can retrieve and reason over.

Experience building and operating large enterprise data pipelines engineered for scale — systems serving 1,000+ users (or equivalent data volume / tenancy), where reliability, isolation, and cost at scale were real constraints you solved.
Strong SQL and Python, with production experience in a modern data warehouse (BigQuery, Snowflake, Redshift, or similar).
Deep familiarity with ETL/ELT patterns, incremental sync, schema design, and data modeling for analytics.
Built and maintained integrations against third-party APIs — OAuth flows, pagination, rate limits, schema drift, and the operational reality of connectors that break.
A bias toward observability and data quality: you instrument your pipelines and you don't ship data you can't trust.
Experience building or operating within SOC 2-compliant systems with enterprise-grade security and privacy — you've handled sensitive customer data under real compliance constraints (access controls, encryption, data isolation, auditability) and treat it as a first-class engineering requirement.

Product Ownership: You'll ship production code daily and help steer key product and technical decisions.
Shape the Engineering Culture: You'll influence how we work—tools, processes, standards, and hiring.
Work with Challenger Consumer Brands: Talk directly to customers (CEOs, CMOs, VP's) of fast-growing consumer brands—some doing $80M–$500M in revenue.

Senior Data Engineer

Key skills