> Markdown version of [/jobs/ext/603307-head-of-data](https://www.wearedevelopers.com/jobs/ext/603307-head-of-data). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Head of Data - **Company:** Tomo Mortgage - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $215,000.0 - $255,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Information Engineering, Standard Sql, SQL Databases, Snowflake, Databricks - **Published:** June 20, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=703e22e47822dd74 ## About the Role Do you have experience in Team development?, Must-haves * AI Fluent, including democratization and enablement of the whole Tomo team. * Strong data science and experimentation foundation: statistics and experimental design. You can independently design and analyze an experiment and defend the result. * Proficient in SQL and the modern data stack. * Range over depth: you've worn multiple hats and can operate credibly across data science, analytics/BI, and data engineering. * Strong business and stakeholder fluency - you translate ambiguous business questions into data work and communicate results to non-technical leaders. * A zero-to-one track record - you've been an early data hire, built a team or analytics function from scratch, or are a senior IC ready to step into your first leadership seat and own the build. Nice-to-haves * Mortgage, fintech, or proptech experience. * Familiarity with our stack: Snowflake, dbt, Databricks, Fivetran. * Hands-on marketing analytics / paid-media measurement. ## Description Tomo is looking for a hands-on data leader to own data end-to-end and build out the function. You'll inherit a mature data platform, a small, high-leverage team. Your mandate will be to build that team out and push the ball forward. This is a player-coach role: you'll be in the work yourself designing experiments, building data models, and partnering directly with the business while standing up the team and processes that let it scale. You'll be the data face to the leadership team, translating questions from across the business - Marketing, Finance, Sales, Capital Markets, Mortgage Ops, Compliance, Product and Engineering - into measurement and models that move decisions., * Own the data function end-to-end - strategy, roadmap, prioritization across a broad stakeholder surface, and the health of the platform. * Build and develop the team - hire across data science, analytics/business intelligence, and data engineering, and grow the people already here. * Drive marketing measurement and experimentation - quantify the effectiveness of marketing spend and build attribution models. * Lead by building - write SQL, build dbt models, and work in notebooks alongside the team; set the technical bar by doing the work, not just reviewing it. * Keep the platform healthy - ensure pipelines and infrastructure are reliable, performant, and cost-efficient as the business scales. * Partner with Product and Engineering - shape how data is instrumented and captured so it's analyzable downstream, and help QA data at the source. * Be a trusted partner to leadership - own the data narrative with the exec team and make the call on what the team does and doesn't take on. ## Related Videos - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Turn Community Events Into a Powerful AI GTM Engine: The Daytona Playbook](https://www.wearedevelopers.com/magazine/732-how-to-turn-community-events-into-a-powerful-ai-gtm-engine-the-daytona-playbook) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 159: AI Pipelines, 10x Faster TypeScript, How to Interview](https://www.wearedevelopers.com/magazine/563-dev-digest-159-ai-pipelines-10x-faster-typescript-how-to-interview)