Effective AI - Data Product Engineer

Effective AI - Data Product Engineer Type: Full-time | On-site | San Francisco, CA Compensation: $230K-$280K + competitive equity Hiring count: 1 Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers). US work authorization required (H-1B transfer, TN, or citizen/GC). Reports to: Arijit (hiring manager) About Effective AI Effective AI is the agent platform for insurance P&L teams, turning messy operating knowledge (filings, regulations, manuals) into governed systems that humans and agents can trust and act on in a $6 trillion industry. It positions itself as the "AI Analyst for Insurance P&L Teams," working alongside existing teams and tools without a full digital transformation. Built by insurance operators solving hard applied-AI problems: long-context reasoning, formal verification, and multi-agent coordination. Founded: 2025 | Team size: 17 to 19 (see data conflict note) | Total funding: $10M seed Backers: Lightspeed, Valor Industry: AI, B2B, Data, Enterprise, Insurance, Security, Software Development Website: effectiveailabs.com Office: San Francisco, CA Why Candidates Should Join Founding data hire: First data engineer on the team; owns the full data layer end-to-end and can grow into leading the entire data function. Zero-to-one from scratch: Shape a foundational system with no playbook at a seed-stage company solving one of the hardest applied-AI problems in a $6T industry. Real AI depth: Work directly on agent reliability, RAG/eval harnesses, and long-context reasoning over unstructured filings, not traditional pipeline plumbing. Strong backing and comp: $10M seed from Lightspeed and Valor, $230K-$280K base plus competitive equity. Intake Call Summary Company: Building an operating system for insurance; expanding data offerings beyond insurance filings to integrate diverse sources (legal, financial records) and unlock new use cases. Role: Data pipeline work plus integrating products for customer use cases, combining data engineering with product/backend development. Not a pure pipeline role. Candidate bar: 5 to 8 years ideal, with system architecture and maintenance skills; open to 4 years if exceptionally strong. Comp/logistics: Intake stated $210K-$270K, max flex to $290K; onsite SF with potential relocation and US visa sponsorship. (Posted band differs, see conflict note.) Ideal profile: Top-30 CS school preferred with flexibility for high-impact candidates; experience across both startups and mid-sized companies, especially data pipeline work. Process: First 10 profiles reviewed together, then screening call, technical screen, onsite. Expect specific questions on past data-integration work and complex projects. Pain points / urgency: High weight on excitement for a foundational build-from-scratch role; watch for candidates actively interviewing elsewhere. Latest HM calibration (Cris McGowan, Aug 12, 2026): Open to candidates without a CS degree if they have impressive qualifications (e.g., math or EE from a top school). Tier B or C colleges will not work unless compensated by high-talent-bar companies/experiences. The team values recent experience at seed to Series D startups and is looking for more aligned profiles. The Role Founding Data Product Engineer building the data layer that powers Effective AI's platform for insurance P&L teams, owning the full path from raw external sources to production-ready signals agents can trust and act on. Roughly 50/50 data infrastructure and product/backend engineering. What You'll Be Doing Work with customers to identify common data use cases (e.g., legal/financial records for company insurance) and decide which external data sources to bring onto the platform Build and maintain end-to-end data pipelines: ingest, extract, and synthesize data from new external sources Expose data through product surfaces so it is easily consumable by agents, ensuring high-quality, accurate answers Build and maintain evaluation harnesses to ensure data quality and agent reliability at scale Tech stack: Python, Data Pipelines, Multi-Agent Systems, LLMs, Search Infrastructure, Git, SQL Qualifications Seniority 5 to 8 years of experience in data engineering, building and operating production data pipelines and systems [Required] Work Experience Built and scaled a data system end-to-end: connected new external data sources, owned ingestion through production [Must have] Recent experience at high-talent-density companies or startups (Seed to Series D, strong bigtech, AI-native companies, fast-moving fintech) [Required] Built agent harnesses or LLM-powered extraction/validation workflows [Strongly preferred] Education CS (or STEM) degree from a top-tier university [Required] Hard Skills Production data pipeline design, ingestion, and orchestration [Must have] Experience with AI/ML agent frameworks and eval harnesses [Required] Large-scale unstructured document processing (PDFs, filings) [Strongly preferred] Miscellaneous Based in SF or willing to relocate; in-office 5 days/week [Must have] Authorized to work in the US (H-1B transfer, TN, or citizen/GC) [Must have] Traits to Avoid Pure ML/data science profile with no data engineering or pipeline ownership Prefers large-company structure and slow iteration cycles Role Details Salary | $230K-$280K (posted); intake stated $210K-$270K, flex to $290K Equity | Competitive equity On-site policy | 5 days in-office in San Francisco, CA Visa sponsorship | Open to visa transfers (OPT, H-1B transfers); US work auth required Employment type | Full-time Location | San Francisco, CA Screening Questions Are you able to work in San Francisco and come into the office 5 days per week? Describe a time you connected or ingested a new external data source into a product. What was the source and how did you make it usable? What's the most complex data project you've built end-to-end? Walk us through what made it hard and how you scaled it. Can you be on-site? If not, are you willing to relocate? What is your salary expectation? How actively are you exploring new opportunities? Interview Process Stage 1 - Submit candidate After submitting, you'll be notified if the hiring manager wants to proceed. Stage 2 - First Round Stage 3 - Second Round Stage 4 - Reference Check Stage 5 - Offer Extended Stage 6 - Candidate Hired Ideal Companies & Backgrounds Updated Jul 29, 2026 Data infrastructure and pipeline companies Databricks, Fivetran, Snowflake, dbt Labs, Confluent, Airbyte, Monte Carlo, Prefect, Dagster Labs High-growth AI startups (seed to Series D) with agent/LLM focus Anysphere, Cognition, Glean, Hebbia, Cohere, Adept Mid-to-large tech companies with strong data engineering teams Stripe, Plaid, Scale AI, Palantir Technologies, Datadog, Figma, Notion, Vercel, Ramp Infra-focused big-tech teams (per HM announcement): Google Spanner/Bigtable, Meta infra, AWS core services. Fast-moving fintech also called out: Chime, Robinhood, Block, Coinbase, Ramp, Mercury. Non-ideal, do not source: Traditional insurance carriers (no startup DNA, not technically challenging): Farmers, Nationwide, State Farm, Allstate, Progressive, Liberty Mutual, GEICO, USAA, Travelers Already thoroughly sourced or off-limits per HM: Rubrik, Nirvana Insurance Note: "Data infrastructure and pipeline companies" listed 9 of 10; 1 company was not expanded before copy and is missing. Ideal Candidate Profiles For reference only, do not source these specific profiles. Muhammad Janjua - LinkedIn Data Engineer at Meta | High-throughput data pipelines & cloud infra | San Francisco Bay Area Strong communication; explained ad-campaign pipeline complexity well Decent day-to-day agent experience; owns data pipelines across multiple teams Weak spots: limited progress on technical task, couldn't explain modeling setup, pipeline explanation skewed business over technical David Lyon - LinkedIn Software Engineer @ Meta | Data Science, ML, Python | Newark, US Feature engineering + SFT to detect bot farms; some pipeline building Weak spots: limited agentic experience, low energy, doubts on seed-stage velocity Vivek Jain - LinkedIn Staff Software Engineer at Databricks | Palo Alto, US HM (Arijit) to share more detail during intake Chetas Joshi - LinkedIn Data & AI @ Robinhood | San Francisco, US Great schools + companies; worked with Arijit at Rubrik; great feedback (not currently looking) Note: "Show all 5 candidates" showed 4 of 5; 1 profile missing (not expanded before copy). Rejected Candidate Feedback Ownership in Production: Prioritize candidates who have built and operated fully productionized data pipelines with clear failure management and quality monitoring. AI/LLM Expertise: Require hands-on, recent experience with AI agents, RAG pipelines, and evaluation harnesses, not just traditional data engineering. Startup & High-Talent Background: Focus on seed to Series D or high-talent tech firms whose experience maps to the end-to-end nature of the role. Top-Tier Academic/Employer Signal: Strong emphasis on a CS or equivalent STEM degree from top-tier schools and companies; non-CS backgrounds require equally impressive top-tier signals. Specific rejection (Jul 30, 2026): One candidate rejected at HM Review, "does not meet our bar on school and employer."