Senior Data Engineer

We are building an AI-native GTM intelligence platform for enterprise revenue teams. Our data is the product. We hold 45M+ companies and hundreds of millions of contacts, built from many vendor feeds and served into search and AI features.

Data quality is our highest priority. Every AI feature we ship is only as good as the data underneath it. So the data engineer is a central role here, not a supporting one.

We are looking for a strong data engineer who can do two things well.

First, the traditional craft. Build reliable pipelines that keep large-scale data correct and fresh. This is a must. Everything else sits on top of it.

Second, the new craft. Use AI to process and clean data at a scale and quality that rules and scripts alone cannot reach.

You will own data as a product, from raw vendor feeds to the clean records the whole platform runs on.

• Data as a product. Own the quality, freshness, and correctness of the data you ship. Treat the teams and features that consume it as your customers. Care that the data is right, not just that the job ran.
• Pipelines at scale. Build and run incremental pipelines that turn messy vendor feeds into clean, trustworthy, queryable data across hundreds of millions of records.
• AI for data processing. Use LLMs and smaller models to parse, classify, extract, and enrich data at scale, and do it cost-effectively.
• AI for data cleaning. Use AI to normalize, deduplicate, and resolve identity, and to catch bad data that rules miss. Match and merge the same company or person across many vendors into one record, and resolve conflicts between sources.
• Correctness you can prove. Build the checks, gates, and observability that catch bad data before it reaches a customer. Be able to show the data is right.
• An AI-native way of working. Use AI tools every day to design, build, and ship faster.

The foundation (must have)

• 5+ years building production data pipelines.
• Strong distributed data processing (Spark or similar) and strong SQL and data modeling.
• Experience with incremental or CDC pipelines, not just full batch reloads.
• The ability to keep large-scale data correct, fresh, and performant.
• You read unfamiliar code and ship without hand-holding.
• Good engineering judgment and clear communication.

What sets you apart (AI-native)

• You use AI tools daily in your own engineering work.
• You have used AI, LLMs, or ML to process, clean, or resolve data, or you are excited to. Not just to write code, but on the data itself.
• You think about data as a product, with consumers, contracts, and quality that is measured.
• You separate “the pipeline succeeded” from “the data is correct,” and you care about the second one.

• Entity resolution, identity graphs, or record matching at scale.
• Applying LLMs or smaller, self-hosted models to data at scale, cost-effectively.
• A lakehouse table format (Iceberg, Delta, or Hudi) and branching (write-audit-publish).
• Vector databases, embeddings pipelines, or Elasticsearch.
• Data-quality frameworks, pipeline gates, and observability.

Our stack (context, not a checklist): Python, PySpark, Apache Iceberg, Project Nessie, Trino, Airflow, Elasticsearch, Postgres, S3, Kubernetes. You do not need all of it. We care more about how you think and what you can build than which exact tools you have used.

• Data is the core of the product, not a side system.
• You will help build an AI-native data platform, not just maintain pipelines.
• Hard, real problems: incremental loads at scale, entity resolution, and AI-driven cleaning across hundreds of millions of records.
• High ownership and direct impact on what ships.
• A small, strong team and a fast pace.
• You report to and learn directly from the Chief Architect.