8+ years in analytics, data science, or analytics engineering with customer-facing data product experience.
Expert in SQL and strong Python skills; hands-on with dbt or Dataform and cloud warehouses like Snowflake.
Daily use of AI tools for coding and testing, with experience evaluating AI output.
Experience delivering data through APIs and data feeds with entitlements and usage tracking.
End-to-end ownership of a company's metric layer, including modeling and governance.
Strong statistical judgment for analysis and forecasting.
Bachelor's degree in computer science, statistics, economics, or related field.
Responsibilities
Transform static reports into self-serve analytics products.
Develop and launch benchmark data products using anonymized data.
Design and manage the data product layer in Snowflake and dbt.
Package data for delivery through APIs and data feeds.
Normalize and resolve messy market data into consistent entities.
Collaborate with the commercial team to validate demand and pricing.
Provide analytics support to the business development team.
Benefits
Medical, dental, and vision insurance options.
Flexible Spending Account.
Paid Parental Leave.
Hybrid Work Policy with 3 days in office.
401(k) plan.
Company-sponsored commuter benefits.
Flexible time off policy.
Catered lunches and snacks.
Monthly wellness reimbursement.
Full Job Description
Data Products Lead
Dealpath is looking for a Data Products Lead to join our growing team and bring our powerful SaaS platform to institutional investment management firms.
This is a revenue role. You will own data products from the first query to the first paying customer and beyond, working directly with our CFO and our business development leaders. You will define what we sell, build it, put it in front of customers, and measure whether they come back and pay. You're excited about this opportunity because you will:
Turn reporting into a product. Replace static reports with self-serve, permissioned analytics. Design how it is packaged and instrument usage so we know who logs in, who comes back and who converts.
Build and launch benchmark data products. Create market benchmarks from anonymized, aggregated data. Own the methodology, refresh SLAs and the quality bar a paying data customer will hold us to.
Own the data product layer. Design governed, multi-tenant models in Snowflake and dbt and the semantic layer in Omni: metric definitions, tests, freshness and correctness alerting, tenant isolation and entitlements.
Deliver data where clients work. Package data for delivery outside a dashboard: APIs, data feeds, warehouse shares and MCP tools. Build in the entitlements, usage metering and SLAs a paying data customer expects.
Normalize messy market data. Data arrives from many sources in different shapes. Resolve it into consistent entities customers can compare, and catch data gaps before a customer does.
Validate demand and pricing with the commercial team. Run design partnerships, time-boxed trials and willingness-to-pay tests. Bring the evidence that decides what we build, how we package it and what we charge.
Be the analytics partner to the business development team. Own the recurring analysis they rely on, including partner reporting, pilot success criteria and revenue attribution, so they can spend their time with customers.
Make it last. Write the metric definitions, document the usage rules for each product and leave work that others can maintain and extend.
How we think about AI for this role
We expect you to build with AI every day. You will use AI tools to write and test code, prototype products and document your work, and you will judge when AI output is accurate enough to put in front of a customer. You will also design data so AI agents, ours and our clients', can query it reliably. What success looks like in your first year
Customers use our data products in place of static reports, and come back on their own.
At least one benchmark product is in market with paying or committed customers.
At least one data product reaches clients through an API, feed or AI agent, not only a dashboard.
Data products are a measurable part of company revenue, with clean attribution.
We're excited that you have:
Commercial instinct. You can sit with a customer, work out what they would pay for, and turn it into a product, a metric and a price test.
Clear writing and speaking. You can explain a methodology to a broker, a product spec to an engineer and a result to a board member.
A bias to ship. You thrive in 0 to 1 work at a growth-stage company and ship in weeks, not quarters.
Qualifications
8+ years in analytics, data science or analytics engineering, including at least one customer-facing data product you shipped to production and supported.
Expert SQL and strong Python. Hands-on with dbt or Dataform, a cloud warehouse (Snowflake, BigQuery or Databricks) and a semantic layer or BI tool (Omni, Looker or similar).
Daily, hands-on use of AI tools (for example Claude, Cursor or similar) and LLMs to write code, test and ship, and experience evaluating their output.
Delivered data to customers through APIs, data feeds or warehouse sharing (for example Snowflake Secure Data Sharing), with entitlements and usage tracking, not only through dashboards.
Owned a company's metric layer end to end: modeling, lineage, governance, tests and adoption by executives.
Statistical judgment: cohort and retention analysis, segmentation, experiment design, forecasting, and knowing when a number is noise.
Bachelor's degree in computer science, statistics, economics or a related field, or equivalent experience.
Nice to haves
Commercial real estate, proptech, marketplace or two-sided network data.
Embedded analytics or data sold as its own product.
Data privacy and aggregation design: de-identification, minimum cell sizes, entitlements.
Built agentic workflows, MCP servers or retrieval on top of structured data.
Hybrid Work Policy, 3 days in office (Tues/Wed/Thur).
401(k)
Company sponsored commuter benefits
Flexible time off policy
Catered lunches and snacks
Monthly wellness reimbursement
The estimated base pay range for California candidates is $190,000 - $215,000.
Your compensation will be based upon several factors including your experience, qualifications, education, location, and the skills assessed in Dealpath's interview process. Your actual compensation will be confirmed in writing at the time of offer.
Dealpath's compensation ranges are determined by current market data, so compensation data posted on our job posts may change as new market data becomes available.
About Dealpath
Dealpath is a cloud-based platform that provides real estate investment and portfolio management solutions. The platform offers tools for deal tracking, pipeline management, and collaboration, as well as analytics and reporting features. Dealpath is designed to help real estate professionals streamline their workflows and make better investment decisions. The company was founded in 2014 and is headquartered in San Francisco, California.
