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Senior Data Engineer

Darkroom

Brazil, India, PakistanremotePosted 18 days ago
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Skill Required

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Key highlights

  • Fully remote role supporting EST time zone (9 AM–5 PM EST)
  • Proven ground with real marketing data at real volume from day one via Darkroom agency
  • Direct access to CEOs, CMOs, and VPs of consumer brands doing $80M–$500M in revenue
  • Unlimited PTO + local holidays
  • Flexible parental leave
  • SOC 2-compliant systems experience required

Role overview

Shadow is building the AI coordination layer for marketing teams — providing shared AI memory, centralized agent control, and model orchestration to help consumer brands shift from workflow automation to agent management. The company was built alongside Darkroom, a performance marketing agency with 10+ years of operation, 100+ employees, 100+ active clients, and experience with over 1,000 consumer brands. This is a fully remote role supporting a team in the EST time zone (9 AM–5 PM EST).

Responsibilities

  • Own the pipelines that bring the world's marketing data into Shadow and keep them fast, accurate, and reliable as the platform scales to thousands of users.
  • 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 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.
  • Partner with the AI and data-science teams to expose clean, well-modeled data the agent can retrieve and reason over.
  • Ship production code daily and help steer key product and technical decisions.
  • Influence how the engineering team works — tools, processes, standards, and hiring.

Requirements

  • 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 — handled sensitive customer data under real compliance constraints (access controls, encryption, data isolation, auditability) and treated it as a first-class engineering requirement.

Nice to have

  • Experience in martech, adtech, or an adjacent data-heavy marketing domain — familiarity with ad platform or ecommerce data, including attribution windows, currency/timezone messes, and deduping across platforms.
  • Familiarity with the Shadow stack: GCP (BigQuery, Cloud Run), PostgreSQL + pgvector, and orchestration/transformation tooling (dbt, Airflow, Dagster, or similar).
  • Experience with pipeline observability and tracing in an AI/LLM context (e.g. Langfuse).
  • Comfort supporting data that feeds AI agents and retrieval systems, not just dashboards.
  • Obsessive about data organization at scale — lives in the data layer and wants to own it end to end.
  • Power AI user — embedded AI into every workflow, thinks in systems rather than one-off prompts, with repeatable structures that compound.
  • Entrepreneurial — doesn't need much direction to move fast, pivots when the situation demands it, and ships production-grade work, not prototypes handed off to someone else.

Benefits

  • Unlimited PTO + Local holidays (relevant to your hub).
  • Remote-First Culture: Many roles are fully remote. Employees based in or near New York or Lisbon HQs are expected to work hybrid with weekly in-office time. Hub locations include Brazil and Spain.
  • Flexible parental leave to support new parents during this important transition.
  • Growth through an interdisciplinary model — every employee gets exposure far beyond their core role, opportunity to grow skills, expand influence, and stay at the forefront of the industry.

Additional details

  • Shadow empowers small teams with technology that makes it easier to market and grow businesses.
  • Shadow is the AI coordination layer — providing shared AI memory, centralized agent control, and model orchestration for marketing teams.
  • The agency behind Shadow is Darkroom — a performance marketing agency operating for 10 years, employing 100+ people, running 100+ clients at a time, and having worked with over 1,000 consumer brands.
  • The agency is both the proving ground and first user, meaning the data built with is real marketing data at real volume from day one, not a synthetic demo.
  • This is a hands-on, build-heavy engineering role for someone who has run large data systems before and wants to do it again in a smaller, faster environment.
  • Every brand connects its full stack (ad platforms, ecommerce, analytics, email/SMS), and the role makes that data land cleanly, normalize into shared schemas, and stay in sync.
  • Talk directly to customers (CEOs, CMOs, VP's) of fast-growing consumer brands, some doing $80M–$500M in revenue.
  • The role is fully remote supporting a team in the EST time zone (9 AM–5 PM EST).
  • Equal Opportunity Statement: dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national orientation, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status; considers qualified applicants regardless of criminal histories, consistent with legal requirements.
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