Turn fragmented data problems into trusted enterprise strategy
I help global organizations transform scattered operational pain points into clear data strategies, quick wins, long-term roadmaps, and trusted foundations for executive decisions, analytics, and AI.
English / Spanish: available for roles and collaborations in Spain, Europe, North America, and remote teams. Estrategia global de datos, gobierno de datos y fundamentos preparados para IA.
Data Strategy Lead, DataArt
Valencia, Spain · Remote worldwide
Areas of Expertise
I work across the core capabilities that make data usable as a managed business asset: strategy, governance, ownership, quality, metadata, data products, and AI-ready foundations.
Enterprise Data Management
Operating models, ownership, standards, and practices that make data reliable at scale.
- Data management strategy and capability assessment
- Domain ownership, stewardship, and accountability models
- Data quality, metadata, lineage, and documentation standards
- Alignment between business priorities and data operating model
AI-Ready Global Data Strategy
Prepare governed, well-documented data foundations so AI initiatives can scale responsibly.
- AI data readiness across sources, mappings, and controls
- Metadata design for assistants, automation, and analytics
- Governance touchpoints for AI-enabled data use cases
- Prioritisation tied to business value, risk, and maturity
Governance Transformation
Move governance from policy documents into decisions, roles, workflows, and delivery practice.
- DAMA-aligned requirements and capability mapping
- Ownership, policy, quality, and stewardship design
- Roadmaps based on maturity, adoption, and business impact
- Executive-ready narratives for investment and change
Data Product & Platform Roadmaps
Translate strategy into data products, backlogs, platform priorities, and measurable outcomes.
- Product vision, success metrics, and delivery priorities
- BRDs, user stories, acceptance criteria, and dependencies
- Impact analysis, schema evolution, and adoption planning
- Delivery partnership with engineering and analytics teams
Metadata, Quality & Lineage
Create the transparency and controls required for trusted analytics, reporting, and AI use.
- Source-to-target mapping across enterprise systems
- Quality rules, validation workflows, and KPI definitions
- End-to-end lineage and transformation documentation
- SQL, Python, Tableau, Power BI, and enterprise system context
Stakeholder & Change Leadership
Align executives, business teams, data owners, and engineering around a shared direction.
- Workshops, requirements sessions, and transformation facilitation
- Translation between executive priorities and technical delivery
- Documentation for policies, flows, transformations, and mappings
- Remote and distributed team leadership across regions
Selected Impact
Transformation work at DataArt, with a focus on global finance data strategy, data quality, ownership, transparency, governance capability mapping, and trust in data for executive management.
Global finance data strategy
Created a data strategy for the finance department of a global company that had no clear data strategy, only separated information about operational problems across teams and systems.
Operational problems translated into governance priorities
Gathered separated business and technical inputs and structured them into governance capability areas to show where data quality gaps, ownership gaps, and transparency issues mattered most.
Quick wins and long-term roadmap
Defined a strategy path that separated immediate improvements from longer-term transformation needs, making the roadmap understandable for business, data, and executive stakeholders.
Board-level decision enablement
Led integration of NetSuite as a financial source and extended board-level decision support on top of the broader finance data management direction.
How I lead data strategy work
In practice, transformation succeeds when business priorities, ownership, governance, data quality, metadata, and delivery roadmaps are designed together instead of treated as separate workstreams.
Clarify business priorities and data decisions
Work with stakeholders to define the decisions, risks, reporting needs, and AI or analytics ambitions the data programme must support.
Assess current data management capability
Collect needs from business, finance, analytics, governance, and engineering teams, then organise them into a coherent view of gaps and dependencies.
Map needs to governance and DAMA capability areas
Classify requirements across governance, quality, architecture, metadata, lineage, ownership, and related domains so priorities are explicit.
Define strategy, roadmap, and operating model
Translate the assessment into a data management strategy, success metrics, standards, governance roles, and accountable ownership.
Lead delivery, adoption, and continuous improvement
Stay close to product, engineering, and analytics work on models, pipelines, documentation, reporting, and AI readiness so the strategy shows up in day-to-day use.
Data Strategy Lead, DataArt
Valencia, Spain
Why work with me?
I am a Data Strategy Lead focused on helping organizations turn fragmented operational problems into clear, trusted, AI-ready data strategies. My work sits at the intersection of business strategy, governance, product thinking, and delivery — where the goal is not just to analyze data, but to create the conditions for better decisions and long-term data management maturity.
My foundation is in requirements management, data modeling, and analytical problem-solving. Over time I moved from delivering insights to owning how data is structured, governed, documented, and trusted. Today I lead initiatives that define data strategy, improve governance, and help organisations shift from fragmented reporting and operational pain points to clear roadmaps, ownership, quality, and transparency.
Before DataArt, I spent nearly six years at OT-OIL leading delivery of planning and monitoring systems for major energy companies, including team leadership, client turnaround, and product replication across multiple sites.
Core strengths
- Data strategy for global finance and enterprise functions
- Data ownership, metadata, lineage, quality, and validation
- Stakeholder alignment, operating model design, and roadmap ownership
- AI-ready data foundations and change adoption
Methods & delivery context
Let's talk about data strategy
Open to conversations with recruiters, data leaders, and data professionals about enterprise data management, governance transformation, AI-ready data strategy, and data product operating models.
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