Dealpath Data Products Lead at Dealpath owns data products from development to revenue generation, working closely with CFO and business development. Designs self-serve analytics, benchmark products, and data delivery methods.
Responsibilities
Turn reporting into self-serve, permissioned analytics products
Build and launch benchmark data products from anonymized, aggregated data
Own the data product layer including multi-tenant models and semantic layers
Deliver data through APIs, data feeds, warehouse shares, and MCP tools
Normalize messy market data into consistent entities
Validate demand and pricing with the commercial team
Partner with business development on recurring analysis and revenue attribution
Write metric definitions and document usage rules for maintainability
Qualification
8+ years in analyticsExpert SQL and strong PythonDailyStatistical judgmentBachelor's degree in computer scienceCommercial real estateData privacy and aggregation designBuilt agentic workflowsThe Perks & CultureMedicalFlexible Spending AccountPaid Parental Leave
Required
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.
Preferred
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.
