You will build and lead the team that turns the data into something the business can actually run on. Company operates across several verticals and markets: digital goods, ticketing, travel and trading, in Iraq, the UAE and KSA. Each has grown its own reporting, its own metric definitions and its own version of the numbers, so basic questions take days and the answers do not always agree.

You will hire the team, own the data platform end to end, from ingestion through warehouse and modelling to the analytics layer on top, and make trusted data a capability the rest of the org can rely on rather than a queue it waits behind.

What you'll do

• Build and lead the data team: Hire, mentor and grow data and analytics engineers, and set the bar for a function that every other team depends on daily.
• Own the technical strategy and architecture for the data platform: Ingestion from operational systems and third parties, warehouse and modelling layer, orchestration, and the semantic layer that keeps metrics consistent across verticals.
• Establish a single source of truth for core business metrics: With definitions agreed with commercial, finance and product, and pipelines that make the numbers reproducible and trusted.
• Deliver self-serve analytics: So most questions are answered without a ticket, and the team's time goes to platform work rather than ad hoc pulls.
• Treat data as a product and own its reliability: Freshness SLAs, lineage, testing, and alerting that fires when something breaks upstream rather than when a stakeholder notices.
• Partner with security and legal on governance: Access control, retention and the handling of PII (passport and telecom identifiers) across markets with different regulatory expectations.
• Build the foundation the next stage depends on: Event tracking standards, the data that data science, personalisation, fraud and experimentation will need.
• Partner with product and engineering teams: So instrumentation and data contracts are designed into services rather than bolted on afterwards.

What you'll bring

• 8+ years in software or data engineering, with 3+ years leading engineering teams, including hiring and growing engineers.
• Direct experience building and operating a production data platform: Batch and streaming ingestion, warehouse modelling, orchestration, and the operational discipline to keep it reliable at scale.
• Strong hands-on technical judgment to lead design reviews and make build-vs-buy and cost tradeoffs on warehouse and tooling spend.
• Deep SQL and dimensional modelling, and comfortable in a Python, TypeScript, Go, Postgres and AWS environment.
• Track record of getting an organisation to agree on metric definitions, not just shipping pipelines. Much of this role is aligning people who currently disagree about what a number means.
• Governance instincts: Access, PII and retention should be treated as design inputs rather than an audit exercise.
• The judgment to sequence a platform build so that it has to be built without laying a foundation that will need to be torn up in a year.

Nice to have

• Experience standing up a data function from zero, or consolidating fragmented reporting across business units.
• Product analytics experience on e-commerce, marketplaces, payments or fintech.
• Experience supporting data science and ML in production: Feature pipelines, model serving or MLOps.
• MENA or emerging-market experience, including multi-currency and multi-market reporting.