Data Product Engineer

Effective AI is building the operating system for the insurance industry by transforming fragmented enterprise knowledge into trusted AI-native workflows.

The platform combines large language models structured data retrieval systems and multi-agent architectures to help insurance organizations reason over complex documents regulations filings legal records and operational knowledge with significantly greater speed and accuracy.

Backed by Lightspeed and Valor with a $10M seed round Effective AI is one of the fastest-growing applied AI startups tackling one of the worlds largest industriesa $6 trillion insurance market. The company is building production AI systems focused on long-context reasoning formal verification retrieval and multi-agent coordination while working directly with customers solving real operational problems.

As the companys first Data Product Engineer youll own the complete data layer powering the platformfrom ingesting raw external data sources to building production-grade pipelines evaluation systems retrieval infrastructure and customer-facing product experiences consumed by AI agents.

This is a rare opportunity to become the founding data engineering leader at an AI-native startup combining product engineering backend development data infrastructure and applied AI into one highly impactful role.

• Build and own the companys end-to-end data platform from ingestion through production deployment

• Work directly with enterprise customers to identify high-value external data sources and new product opportunities

• Design build and operate production data pipelines for structured and unstructured datasets

• Integrate legal records financial documents insurance filings PDFs and other complex enterprise data into the platform

• Develop scalable ingestion extraction transformation and orchestration systems supporting AI agents

• Build evaluation harnesses to monitor data quality agent performance and production reliability

• Design search infrastructure enabling fast accurate retrieval for LLM-powered applications

• Build data products that expose clean production-ready information directly to AI agents and customers

• Own schema evolution monitoring pipeline reliability and production maintenance

• Collaborate closely with product engineering and customers to translate ambiguous business problems into scalable technical solutions

• Build AI-powered workflows using modern LLMs retrieval systems and agent frameworks

• Establish engineering best practices across data architecture testing deployment and reliability

• Operate with founder-level ownership in a fast-moving startup environment while helping define the companys long-term data strategy

• 58 years of professional software or data engineering experience

• Experience building and operating production data systems end-to-end

• Experience owning data platforms from initial ingestion through production deployment

• Experience at high-growth startups or rapidly scaling technology companies

• Experience building systems through both early-stage development and production scale

• Strong product mindset with the ability to connect technical decisions to customer value

• Experience working with AI-native products or modern machine learning systems

• Comfortable operating independently without established processes or playbooks

• Demonstrated ownership of production infrastructure reliability monitoring and maintenance

• Leadership potential with interest in growing into ownership of an engineering function

• Expert Python engineering experience

• Strong SQL and relational database expertise

• Deep experience building production data pipelines and orchestration systems

• Experience with large-scale unstructured document processing (PDFs filings legal records financial documents)

• Experience building AI-powered workflows using LLMs retrieval systems or agent frameworks

• Experience with evaluation frameworks testing infrastructure or model quality systems

• Experience designing scalable data architectures supporting production applications

• Strong backend software engineering fundamentals

• Experience integrating external APIs and complex third-party data sources

• Experience building production search infrastructure or retrieval systems preferred

• Familiarity with modern multi-agent architectures RAG pipelines or AI orchestration frameworks is highly desirable

• Bachelors or higher degree in Computer Science Engineering Mathematics Physics Electrical Engineering or another STEM discipline preferred

• Strong technical foundation with demonstrated engineering excellence

• Candidates from top technical universities are preferred though exceptional industry experience is equally valued

• Exceptional product thinking

• Strong customer empathy

• Excellent systems thinking

• High ownership mentality

• Strong communication skills

• Ability to simplify complex technical concepts

• Comfortable working through ambiguity

• Strong execution orientation

• Collaborative low-ego mindset

• Passion for building foundational AI infrastructure

• Curiosity around emerging AI technologies and modern engineering workflows

• Base Salary: $230000 $280000

• Competitive Equity Package

• Founding Data Product Engineer opportunity

• High ownership with direct influence over product architecture

• Work alongside experienced AI researchers and engineering leaders

• Opportunity to build foundational infrastructure powering enterprise AI agents

• Exposure to cutting-edge LLM systems multi-agent architectures and retrieval infrastructure

• Well-funded Seed-stage company backed by Lightspeed and Valor

• Significant career growth with opportunity to build and lead the future data organization

• Work onsite with a highly collaborative engineering team in San Francisco

This is an opportunity to become the first Data Product Engineer at one of the most ambitious applied AI startups in enterprise software.

Youll build the foundational data platform powering intelligent AI agents that reason over some of the worlds most complex enterprise information while helping define how production AI systems consume validate and reason over trusted data.

If you enjoy building production data systems owning products from zero to one working directly with customers and operating at the intersection of data engineering backend systems and applied AI this role offers exceptional ownership technical depth and long-term impact.