Senior Data Engineer

Our client is one the fastest growing, AI for Marketing tech firms in the US. Their platform lets marketers focus on telling the story of their brand while AI agents handle the operationally intensive work: data management, analytics, campaign generation, measurement and reporting. They work with leading consumer brands across categories, including the NBA, Capital One, Hard Rock Stadium Group / Miami Dolphins, Wander and Trust & Will. We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence and Lazard, alongside researchers from Berkeley, MIT, Stanford and Cambridge. They are now searching for a talented, product related, Senior Data Engineer based in their offices in New York. Key Requirements 5+ years in data/software engineering in a data-heavy context, with real ownership of a production data platform at scale (think XX+ TB, many sources, many consumers) Expert Python and SQL, fluent enough that ingestion and modeling are reflexes, not projects Deep experience with transformation and orchestration tooling (SQLMesh, dbt, Airflow or equivalent) and a point of view on where they break and what comes next Strong grasp of analytical warehouses (Snowflake/Redshift/BigQuery) and how they interplay with transactional stores (Postgres/MySQL) A track record of multi-tenant design: isolation, governance and secure access patterns as tenant count climbs Demonstrated judgment on the stability vs. speed of development tradeoff: you've kept a platform reliable for its users while still letting a team ship fast on top of it, and you can articulate how you decided where each belonged Experience evolving a schema, data model or ontology in production (versioning, migration and backward compatibility) without breaking downstream consumers Interest in (or experience) exposing data to AI agents (MCP, vector search, semantic layers, retrieval) and a conviction that agentic access is a first-class way to deliver a data product Comfort owning ambiguous, cross-cutting initiatives end-to-end in a lean, fast-changing environment. Willing to work onsite in NYC (relocation provided) Lakehouse or Spark experience and comfort handling XX+ TB datasets Built or operated a CDP, identity graph or reverse-ETL/activation system Strong communication skills, preferably has interfaced with enterprise customers before Data lineage and reactive-propagation systems; streaming (Kafka/Flink/Snowpipe) Backend or ML/AI-eng exposure; feature engineering and training infra are adjacent to this role Heavy and efficient use of AI coding tools (Claude Code, Cursor, etc.) as a force multiplier Built infrastructure for agentic or LLM workflows on top of data (OpenAI Agents SDK, MCP servers, vector DBs, retrieval and eval harnesses) Prior early-stage start-up experience #J-18808-Ljbffr Assembl