Head of Data

Ziina is looking for a Head of Data to build and lead our new Data team. This role is an exciting opportunity to make AI, predictive analytics, and experimentation part of how every team at Ziina works. That could mean CX forecasting next month's ticket volume to plan staffing, BD reaching the right merchant segments, or Product building intelligent search and features that anticipate what our users want to do next. We're at a major inflection point in our growth. We have rich payments data, a strong platform foundation, and a founding team of data platform and AI engineers already in place. We're looking for the right leader to set the vision and turn it into platforms people actually use.

As the founding leader of a small, agile team, our ideal candidate is a hands-on builder with the vision to design platforms from the ground up and the judgment to know which to build first. You'll thrive in our fast-paced environment by making tradeoffs, shipping early use cases that prove value, and turning them into self-serve tools the rest of the company can use. We're looking for someone who is as ambitious as we are to deliver high-quality products to our users. In short, we want owners who are ready to build big things with us.

As a Head of Data at Ziina you will:

Define and own the vision, strategy, and roadmap for data, ML, and AI at Ziina, focusing investment where it creates the most value across product and operations

Evolve the existing data foundations: a trusted, well-modeled warehouse and semantic layer with clear ownership, quality checks and lineage, so data is easy for both people and AI agents to find, understand and use

Lead, grow, and mentor the Data team, starting with our data platform engineers and AI engineer, and hire the next wave of engineers and scientists as we scale

Build the self-serve ML, LLM, agent and experimentation platforms that let every team at Ziina put AI to work safely and measurably

Partner with every function including Product, Fincrime, CX and Finance to find high-impact use cases, ship the first versions alongside them, and turn what works into repeatable, self-serve tools

Establish AI and model governance in line with regulatory expectations for licensed financial institutions, including model risk management, monitoring, bias testing, explainability, and human oversight

Key skills and characteristics

Have 8+ years of experience in data, ML, or AI engineering, including 3+ years leading teams that shipped ML or AI systems to production

Have built or run core ML platform infrastructure (feature stores, training pipelines, model registries, real-time serving, and monitoring) and understand the build-vs-buy tradeoffs at startup scale

Have shipped LLM-powered products or internal tools to production, with hands-on experience in evals, prompt iteration, retrieval, and model routing or cost optimization

Have hands-on experience with agent frameworks and tool-use protocols such as MCP, and a clear point of view on giving agents access safely

Understand experimentation deeply, including experiment design, statistical power, guardrail metrics, and the common ways A/B tests mislead

Are still hands-on: comfortable writing Python and SQL, reviewing designs, and building alongside a small team

Can translate technical work into business outcomes and influence leaders in non-technical teams

Are based in, or open to relocating to, the UAE

What would amaze us

Proven experience building data and AI platforms at a fintech, payments company, or other regulated, high-throughput business

A track record of taking a Data function from zero (or near-zero) to a platform used across the company

Production ML experience in payments, such as fraud, risk, forecasting, personalisation, or merchant intelligence

Hands-on experience with model risk management and AI governance under a financial regulator

Experience building AI products for Arabic-speaking users or MENA markets

Active engagement in the tech community through open source contributions, conference speaking, or technical writing

Our tech stack

We've built a modular architecture with the principles of reliability, scalability, and maintainability in mind. Our current stack is:

Typescript, Node.js and Nest.js for our main application’s backend.

Next.js, React and TypeScript for our web applications.

Swift/SwiftUI for our iOS app, Kotlin for our Android app.

PostgreSQL for consistent and durable storage, Redis for quick fetching, Elasticsearch for quick searching.

Snowflake for our data warehouse, dbt for transformations, and Metabase for BI.