Data Management Jobs in California (NOW HIRING)

Head of Data Strategy & Operational Intelligence

This is a true 0-to-1 mandate at a fast-scaling, profitable SaaS fintech. You will build the operating data foundation that determines how the company defines, measures and trusts performance.

Many critical metrics today depend on manual, human-owned processes and competing definitions. You will establish canonical KPIs, decision rights, governance and an operating rhythm that create one trusted source for leaders across the company, from operating teams through the CEO.

Two things make this role unusual.

First, this is as much an operating-transformation role as it is a data-leadership role. The challenge is not only building pipelines. It is aligning finance, sales, product and operations on what “customer,” “revenue” and other core measures mean, then establishing governance that holds when stakeholders disagree.

Second, the mandate starts with a team at zero. You will hire and develop a small, senior group of data, analytics and software engineers capable of delivering company-wide impact without building a large organization.

What you will own:

• Map the company’s current KPIs, definitions, sources, owners and manual processes.

• Set the company-wide data and operational-intelligence strategy, including a multi-quarter roadmap across several business domains.

• Establish canonical metric definitions, clear decision rights and governance that prevents competing versions of the truth.

• Reconcile systems that describe customers and revenue differently, including transactional or product data, CRM and billing.

• Evaluate the existing data foundation, identify what is missing and make thoughtful build-versus-buy decisions involving governance, semantic layers, modern analytics products and AI-enabled tools.

• Drive adoption across business and engineering teams, including when priorities compete or stakeholders disagree.

• Design the organization, determine the hiring sequence and recruit and develop the team from the ground up.

You may be a strong fit if:

• You have led a company-wide data transformation in a post-product-market-fit business, taking it from strategy through implementation and durable adoption.

• You have owned what a metric means, not only how it is calculated or displayed. You have arbitrated competing definitions and created governance that made one trusted definition stick.

• You have built a multi-quarter roadmap across several business domains and can explain what you prioritized, what you deferred and why.

• You have deeply reconciled at least one or two of the following: product or transactional data, CRM and billing. Depth in two matters more than light exposure to all three.

• You have influenced C-suite decisions and carried a company-level program through competing priorities or executive resistance.

• You have personally designed, hired and developed a technical data organization. You can describe your first hires, the bar you set and people who grew under your leadership.

• You can move from an executive conversation about company strategy into the mechanisms behind data architecture, metric governance and tool selection.

• You have practical, recent experience evaluating or adopting modern analytics and AI-enabled tools. You have a grounded point of view on where they create value, where they do not and how their quality should be measured.

• You are motivated by company-wide impact and ownership rather than by the number of people reporting to you.

Experience in fintech or another regulated, high-stakes data environment such as healthcare, insurance or financial services is strongly preferred.

This role is likely not the right fit if:

• Your experience is primarily building data platforms or infrastructure without owning the business operating model built on top of them.

• You think of yourself primarily as a BI, reporting or dashboarding leader. A separate team owns those capabilities.

• Your leadership experience is still predominantly individual-contributor work.