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

1. Are you able to work in San Francisco and come into the office 5 days per week?
2. 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?
3. 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.
4. Can you be on-site? If not, are you willing to relocate?
5. What is your salary expectation?
6. 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."