Drivetrain is on a mission to empower businesses to make better decisions. Our financial planning & decision-making platform helps companies scale and achieve their targets predictably. Drivetrain is a remote-first company headquartered in the San Francisco Bay Area. Founded in 2021 by a couple of ex-Googlers, Drivetrain is a fast-growing company on a trajectory for success with backing from leading venture capital firms. Drivetrain provides a great culture for its employees to thrive in and be happy. 💜 Remote-friendly: Drivetrain brings together the best and the brightest, no matter where they are and provides them a great degree of autonomy. We trust our people.
🗣️ Open & transparent: We know that when our creators have access to all the information they need, their best work will emerge.
👏 Idea-friendly: We provide an environment to explore new ideas, to take risks, to make mistakes, and to learn, so you can succeed. Anyone in the company can come up with great ideas and become a catalyst for positive change. We let the best ideas win.
👥 Customer-centric: We follow a product-led growth strategy, continuously learning from our customers and collaborating to build the amazing software that Drivetrain is. The Role Everything in Drivetrain starts with data. Before a CFO sees a forecast, we have to ingest data from 800+ source systems, reconcile it, model it, and make it trustworthy enough to run a board meeting on. The Data Platform is that foundation. It covers integrations, pipelines, the semantic and metrics layer, data quality, and the infrastructure our AI agents reason over. As Product Manager, Data Platform, you'll own this foundation end to end. You'll sit at the intersection of engineering, finance, and AI, turning messy real-world finance data problems into platform capabilities that make onboarding faster, data more reliable, and our AI features smarter. This is a platform role with direct customer impact: every improvement you ship shows up in how quickly customers get value from Drivetrain. What You'll Do Own the Data Platform roadmap, spanning integrations and connectors, ingestion pipelines, data modelling and transformation, the metrics and semantic layer, and data quality and observability.
Decide which source systems to build, deepen, or buy (ERP, CRM, HRIS, billing, and warehouses like Snowflake and BigQuery), and cut customer onboarding time from weeks to days.
Partner with AI/ML engineers so our agents get clean, well-modelled, context-rich data. Define the structures and APIs that let AI features reason reliably over financial data.
Spend real time with CFOs, FP&A teams, and our implementation team to understand how chart of accounts mapping, multi-entity consolidation, currency, and reconciliation actually break in practice.
Write clear PRDs, data contracts, and acceptance criteria that backend and data engineers can build against, and run discovery, sprint planning, and launches with engineering.
Own platform health metrics such as sync reliability, data freshness, error rates, time to first insight, and self-serve onboarding rate.
Use AI in your own work every day to prototype, automate analysis, and push the team toward AI-first answers to platform problems. What You Bring 2+ years in product management, including at least 2 to 3 years owning a data platform, data infrastructure, integrations, or API product at a B2B SaaS company.
Strong data fluency. You understand ETL/ELT, data modelling, warehouses (Snowflake, BigQuery, Redshift), APIs, and tools like Fivetran or dbt well enough to debate trade-offs with engineers, and you're comfortable writing SQL.
AI and automation fluency is non-negotiable. You use LLMs and AI tools daily to research, prototype, analyse, and ship faster, and you have clear opinions on what it takes to make AI work reliably on structured data. Platform thinking. You design for reuse and scale, balance internal and external customers, and know when to build a generic capability versus solve one customer's problem.
Equally at home on a call with a CFO in the morning and in a schema review with engineers in the afternoon.
Clear writing. Your specs, decision docs, and updates are sharp, structured, and easy to act on.
A bias for action. You do well in a fast-moving startup, handle ambiguity, and own outcomes rather than outputs. Nice to Have Exposure to finance or accounting data such as GL, chart of accounts, ERPs (NetSuite, Sage Intacct, QuickBooks), billing systems, or FP&A workflows.
Experience building products on top of LLMs, RAG, or agentic systems, especially over structured or tabular data.
A background in data engineering, analytics engineering, or software engineering before moving into product.
Experience with a semantic or metrics layer, data catalog, or data quality tooling.
A consulting, finance, or analytics background that gives you a feel for how business users actually consume data. What Success Looks Like In 30 days: you know our data architecture, top integrations, and biggest onboarding pain points, and you've spoken with customers and the implementation team.
In 90 days: you've shipped a clear Data Platform roadmap and delivered first wins on integration reliability or onboarding speed.
In 6 to 12 months: onboarding is measurably faster, data issues are caught before customers see them, and the platform is a visible accelerator for our AI roadmap. Why Drivetrain Own a foundational product area at an AI-native company, with real scope and direct access to leadership.
Solve hard, high-stakes problems. Finance data has to be right, every time.
Work with a sharp, AI-first team across India and the US that ships fast.
Competitive compensation, meaningful equity, and a culture that rewards ownership.