Data Quality Agent Jobs (NOW HIRING)

Data Product Engineer

Location: San Francisco / In-person preferred
Employment Type: Full-time
Experience: 5–8 years
Focus: Data Products, AI Agents, Insurance, Data Pipelines, Evaluation Systems

About Our Client

Our client is building an AI-native platform for insurance profit and loss teams.

The company is focused on helping insurance organizations make better, faster decisions by turning fragmented external data into trusted signals that AI agents can understand, reason over, and act on.

Backed by Lightspeed and Valor, our client has raised $10M in seed funding and is building foundational technology for one of the largest industries in the world. This is an opportunity to join as a founding engineer and help shape the data layer of the platform from the ground up.

About the Role

Our client is hiring a Data Product Engineer to build the data infrastructure and product layer that powers their AI platform.

This role sits at the intersection of data engineering, product engineering, customer discovery, and AI systems. You'll work directly with customers to identify high-value data use cases, determine which external data sources matter most, and own the full path from raw source data to production-ready signals.

The ideal candidate is someone who can build reliable data pipelines, think deeply about data quality, expose data through intuitive product surfaces, and create evaluation systems that ensure agents can produce accurate and trusted answers.

What You'll Do

• Work directly with customers to identify common, high-value insurance data use cases

• Determine which external data sources should be brought into the platform

• Build and maintain end-to-end data pipelines across ingestion, extraction, synthesis, and transformation

• Turn raw legal, financial, company, and insurance-related records into trusted production signals

• Expose data through product surfaces that are easy for AI agents and users to consume

• Build systems that help agents answer questions accurately using reliable data

• Create and maintain evaluation harnesses to measure data quality and agent reliability

• Partner closely with founders, customers, and technical teams to shape product direction

• Own core pieces of the data layer from scratch as an early engineering hire

• Build scalable foundations for a platform serving insurance teams in a massive market

What We're Looking For

• 5–8 years of experience in data engineering, product engineering, or software engineering

• Strong experience building production data pipelines and data products

• Ability to work directly with customers and translate business problems into technical systems

• Strong understanding of data ingestion, extraction, transformation, synthesis, and quality

• Experience working with messy external data sources

• Strong product instincts and the ability to build data systems that are useful to end users

• Experience building systems that support AI agents, LLMs, retrieval, or automated workflows

• Comfort creating evaluation frameworks for data quality, accuracy, and reliability

• Strong ownership mindset and ability to operate in an early-stage startup environment

• Clear communication skills and comfort working across customers, founders, and engineering

Bonus Experience

• Experience in insurance, fintech, financial services, legal data, compliance, or regulated industries

• Experience building AI-native products or agentic data workflows

• Experience with document extraction, entity resolution, data enrichment, or external data integrations

• Experience building evaluation harnesses for LLMs, agents, or data pipelines

• Experience as a founding engineer or early engineer at a seed-stage startup

• Experience turning ambiguous customer problems into scalable product infrastructure

Why This Opportunity

• Join as a founding engineer at a $10M seed-stage company

• Build the foundational data layer for an AI-native insurance platform

• Work directly with customers to identify and solve high-value data problems

• Own the full path from raw external data to trusted agent-ready signals

• Partner closely with the founding team and shape the technical direction early

• Build in a $6T industry with massive opportunity for AI-driven transformation

• Work on hard problems across data quality, AI reliability, product surfaces, and agent trust