About the Team
The Be Data department owns the end-to-end data foundation serving every business line at beGroup — Transport, Food, Delivery, Fintech and others. Beyond technology, the team is the bridge that connects users and functions through trusted data. We run a modern data platform on Google Cloud (BigQuery, Airflow, dbt/Dataform) with Power BI and Superset for business intelligence, and we are building toward a governed semantic layer and self-service analytics.
About the Role
The Head of Data Platform & Analytics owns the trusted-data supply side of the Data organization. The first priority is stakeholder management and company-level alignment — aligning business and functional heads on one shared set of metric definitions (a single source of truth), clear data ownership, and responsibilities.
Around that priority, the role also leads:
- BI & Analytics — a company-wide analytics function (standardized metrics, dashboards, data self-service) that puts trusted numbers in decision-makers hands, quickly and increasingly self-served.
- Data platform & governance — a reliable, secure and cost-efficient platform, and an enforced governance model (semantic layer / SSOT, data-gate, Data Dictionary) — run through the pod leads (Data Engineering; BI, Analytics & Governance).
- People & capability development — growing the pod leads and their teams (analysts, BI, engineers), raising the bar on delivery and quality, and building the bench.
You own the outcomes and lead through your pod leads, not as an individual contributor.
Job Responsibilities
- Company-level alignment of metrics, ownership & responsibilities. Expand and maintain the single source of truth: align business and functional leaders on metric definitions, formulas and sources; assign and maintain data ownership, roles and scope for every critical asset and demand; and act as the trusted arbiter when resolution is needed.
- Senior-stakeholder management & demand governance. Be the senior counterpart to vertical and functional heads: manage expectations, priorities and SLAs; run a demand-based capacity model with clear top-up rules; and inject requirements for cross-cutting workstreams so shared needs aren't stuck behind any one vertical's backlog.
- Data governance & the release data-gate. Build out and enforce the governance model — Data Dictionary adoption, the data-gate in the release cycle, and data-quality standards — so ownership and definitions are lived in practice, not just documented.
- Analytics & BI leadership. Own BI and analytics delivery — the Weekly Business Review, standardized metrics, dashboards by level/function, self-service enablement and SLAs — as the vehicle that puts trusted numbers in decision-makers hands (lower time-to-insight, higher % self-served).
- Platform reliability & cost (with the Data Engineering lead). Own platform outcomes — availability/SLOs and spend within budget (FinOps, reservation/quota governance) — partnering with and directing the Data Engineering lead and team, who own hands-on platform administration and tooling.
- People & delivery leadership. Lead and grow the pod leads and their teams; manage workload, hiring and quality; represent the Data organization to senior leadership and cross-functional partners.
Requirements
Must-have
- Executive-level stakeholder management — a track record of aligning business and functional leaders on metric definitions, data ownership and priorities, and holding the line as the arbiter of trusted numbers.
- 8+ years in data/analytics, including several years leading teams (managers or senior individual contributors).
- Strong in data modeling, BI & analytics, and metric / semantic-layer design — able to set the standard for how the company measures itself.
- Proven data governance ownership: SSOT / metric definitions, data ownership models, data dictionary, data-quality and access discipline.
- A service mindset and excellent communication — this role exists to make the whole company more decision-ready.
Preferred
- Hands-on data engineering / platform administration (BigQuery administration, pipelines, IaC, FinOps) — or a demonstrated ability to lead a strong data-engineering team to these outcomes without being the deepest engineer in the room.
- Experience running self-service analytics / BI at organizational scale, and managing a budget / resource envelope.
- Cloud cost optimization; data-catalog / lineage tooling (OpenMetadata, Dataplex or similar).
