Head of Data Engineering & Data Products

Spain·BarcelonaFull-timeexecutive

EngineeringData Engineering

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Key Responsibilities

Requirements Summary

Establish scalable data foundations supporting intelligent automation and AI-supported operational work

Technical Tools

EngineeringData Engineering

~1 min read

→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

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

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

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

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.

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

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

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

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

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

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

~1 min read

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)