Head of Data Engineering & Data Products

Posted 8/13/2026 · last confirmed live 8/26/2026

Job Title: Head of Data Engineering & Data Products Location: Porto, Portugal (preferred) or Barcelona, Spain (optional) Type: Full-time – Hybrid working Reports to: Group CITO Unilabs is on a multi-year journey to become Europe’s leading diagnostics company. To achieve this, we are strengthening our ability to operate at scale across markets, leverage synergies across our network, and continuously evolve to meet the changing needs of patients, clinicians, and healthcare ecosystems. As part of our broader transformation journey to build a more agile, efficient, and patient-centred organisation, while strengthening our operational, medical, and commercial performance, we are looking to recruit a Head of Data Engineering & Data Products based in Porto, Portugal. This role is central to establishing scalable enterprise data foundations within UniTech, transforming a fragmented landscape into a trusted, product-driven data capability that enables operational reporting, self-service analytics, intelligent automation, and data-driven decision-making across Unilabs geographies. The Head of Data Engineering & Data Products will define and execute the enterprise data platform strategy, building scalable and reusable data capabilities serving country and functional needs across core operational domains including operations, finance, sales, and HR. The immediate focus over the next 12–18 months will be on: establishing strong enterprise data engineering capabilities, delivering trusted operational reporting, productizing fragmented reporting and analytics into reusable enterprise data products, enabling self-service operational insights, and creating scalable data foundations for future intelligent automation and AI-supported operational workflows. The role requires a pragmatic, delivery-oriented leader capable of balancing speed, usability, governance, and scalability while driving measurable operational value across Unilabs. The current team comprises approximately 10 professionals and is expected to evolve over time across three closely connected capability areas: Data Engineering Data Products & Operational Reporting Intelligent / Agentic Automation Key Responsibilities: 1. Enterprise Data Platform Strategy & Engineering: Assess current maturity and define a scalable enterprise data platform strategy serving all markets Drive a platform-based approach leveraging modern technologies (e.g. Azure, Fabric, Databricks or equivalent) Ensure scalable and secure: o multi-country data ingestion and harmonization o processing and storage capabilities o access management and compliance controls Establish reusable integration and data engineering patterns across enterprise and operational systems 2. Data Products & Operational Reporting: Drive the transition from fragmented reporting toward reusable enterprise data products Establish scalable data products across core domains: o Operations o Finance o Sales / Commercial o HR Productize operational and management reporting into trusted, scalable, near real-time self-service capabilities Enable self-service access to trusted operational data for business users, including operational managers and country functions Support business functions in scaling operational transparency and data-driven decision-making 3. Business Alignment & Federated Analytics Enablement: Operate within a federated data and analytics model where business functions continue to define priorities, KPIs, analytical requirements, and use cases Provide shared enterprise capabilities enabling scalable engineering, reusable products, operational enablement, and self-service reporting Partner with business stakeholders to translate operational needs into scalable enterprise data products Prioritize delivery based on measurable operational and business impact 4. Intelligent Automation & Operational Enablement: Establish scalable data foundations supporting intelligent automation and AI-supported operational workflows Enable workflow orchestration and embedded operational decision-support capabilities integrated into enterprise platforms and processes Support integration of automation and AI-enabled operational tooling into the enterprise ecosystem Ensure alignment between intelligent automation capabilities and enterprise data products, integrations, and operational workflows Remain clearly separated from clinical AI development, commercial AI products, and standalone AI research functions 5. Data Integration & Architecture: Define and implement scalable integration patterns across: o enterprise systems (ERP, CRM, HR) o operational systems (LIS, RIS, imaging, operational platforms) Ensure alignment with enterprise integration platforms and API strategies Reduce fragmented and point-to-point data flows through reusable integration and data product patterns Drive semantic consistency and interoperability across enterprise data domains Establish scalable and compliant enterprise data architectures supporting anonymized and cohort-based data provisioning capabilities for approved analytics, research, operational, approved external research and future data-sharing use cases. Ensure appropriate anonymization, interoperability, governance, and traceability principles are embedded into enterprise data products and integration patterns. 6. Lightweight Governance, Security & Compliance: Establish pragmatic and lightweight enterprise data governance principles focused on scalability, usability, and operational value delivery Define and support: o data ownership alignment o semantic consistency o data quality principles o data contract concepts between source systems and consuming products Ensure business accountability for data ownership and usage Ensure compliance with GDPR and relevant healthcare regulations Implement secure and auditable data access principles Define governance and access principles supporting compliant anonymized data usage, cohort-based analytics, and approved external data-sharing scenarios in alignment with regulatory and security requirements 7. Vendor & Technology Management: Define and manage the enterprise data technology stack with strong focus on simplicity, scalability, and cost optimization Evaluate and optimize modern cloud-native capabilities and tooling Manage relationships with data, integration, and automation vendors and partners 8. Strategic & Leadership Capabilities: Demonstrated ability to define and execute enterprise-wide data strategies aligned with measurable operational and business value Strong experience in enterprise data engineering, data products, and operational reporting enablement Proven track record in delivering scalable self-service and data democratization capabilities Ability to operate at executive level, influencing C-level stakeholders and business leaders Strong financial and portfolio management capabilities, including investment prioritization, ROI/TCO assessment, and cost optimization Drive data literacy and data adoption across the organization 9. Domain & Technical Environment: Experience in regulated environments (healthcare strongly preferred) Strong familiarity with modern enterprise data stacks including: o Azure (Fabric), Databricks or equivalent o enterprise integration platforms and APIs o ERP, CRM, LIS/RIS and operational systems integration Experience in workflow automation, orchestration, and operational data enablement environments Experience working within federated or hybrid operating models and decentralized business environments 10. Organizational & Execution Strength: Experience building and scaling high-performing multidisciplinary teams across: o data engineering o analytics engineering o data products o automation and orchestration capabilities Strong vendor and partner management capabilities Pragmatic and outcome-driven mindset balancing: o standardization vs flexibility o speed vs scalability o innovation vs cost efficiency Success Measures: Establishment of scalable enterprise data foundations adopted across markets Delivery of trusted operational reporting and reusable data products across core domains Increased adoption of self-service operational reporting Reduction of fragmented and duplicated reporting solutions Improved operational transparency and data-driven decision-making Enablement of intelligent automation and operational workflow orchestration capabilities Contribution to operational efficiency and value realization Requirements Profile & Qualifications 15+ years of experience in enterprise data, analytics engineering, and data platform leadership, including at least 5 years in senior leadership roles in complex, multi-country environments Proven track record in building and scaling enterprise data platforms and product-oriented data operating models Deep expertise across: o Data Engineering o Analytics Engineering o Enterprise Data Architecture o Data Products & Self-Service Enablement o Enterprise Integration Patterns o Workflow Automation & Operational Enablement Experience leading federated or hybrid enterprise data operating models Strong understanding of governance, interoperability, and scalable enterprise data delivery Experience in regulated healthcare environments and compliance-driven organizations preferred (strong asset)

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