Head of Data Engineering & Data Products

About the role

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 anonymize

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at Unilabs? Share your experience