Enterprise Data Owner AI & Analytics
Contract
Long Term
Remote Project
General Description
The Enterprise Data Owner AI & Analytics is a newly established senior individual contributor role accountable for making ModusLink's enterprise data usable, trusted, and consumption-ready for AI, analytics, and enterprise decision-making. This role does not build databases, pipelines, or warehouses. Instead, it owns the definitions, ownership boundaries, quality expectations, and consumption rules that determine whether data can safely and effectively power AI and analytics use cases across the company.
This role is intentionally pragmatic. It prioritizes speed to business value, reuse of existing platforms, and stability of data used by AI over enterprise architecture perfection. The person in this role will be the single decision authority on what data AI systems may consume, from where, and under what conditions while also acting as ModusLink's authoritative voice on data continuity through the Oracle Fusion transition.
This role also owns selection and implementation of ModusLink's future data lake and lakehouse platform. It will lead vendor evaluation, technology selection, and rollout partnership with IT ensuring the platform is built around real AI and analytics needs rather than architectural theory.
Strategic Context
- ModusLink operates ~20 sites in 12 countries with data spread across SAP ECC, SQL databases, SharePoint, Power BI, and Microsoft 365.
- Oracle Fusion Cloud ERP is being deployed globally, requiring definition continuity and data ownership discipline through the transition.
- AI adoption is accelerating (Copilot, Copilot Studio, Claude via Azure, agentic workflows), creating urgent need for a single point of accountability for AI-consumable data.
- The company has intentionally chosen a lean operating model this role is one of the highest-leverage hires supporting that model.
Key Responsibilities
Own the Data Lake / Lakehouse Decision and Implementation
- Own selection of ModusLink's future data lake / lakehouse platform (e.g., Microsoft Fabric, Databricks, Snowflake, Synapse) based on business fit, cost, and Fusion / Copilot compatibility.
- Lead the implementation roadmap in partnership with IT and AI Engineering including phasing, domain sequencing, and success criteria.
- Define the medallion / zone strategy (raw, curated, consumption) to the level needed for AI and analytics not more.
- Ensure the lakehouse serves as the primary read-optimized layer for AI agents, Power BI, and analytics not as a replacement for systems of record.
- Prevent scope creep the lakehouse is a means, not the mission.
Enable AI and Analytics Velocity
- Prioritize data readiness work based on business value and AI use case impact not architectural completeness.
- Ensure AI agents, Power BI models, and analytics solutions consume trusted, documented, and stable datasets.
- Measure and report the impact of data quality improvements on AI adoption and business outcomes.
- Support rapid, incremental data improvements aligned to the top AI and analytics initiatives.
Own Authoritative Sources and Definitions
- Identify and maintain authoritative sources for high-priority data domains including customer, supplier, product, shipment, inventory, and finance.
- Resolve cross-functional data definition conflicts (e.g., what constitutes an 'active customer,' 'shipment,' 'margin').
- Maintain lightweight documentation of ownership, definitions, and consumption rules sufficient for AI and analytics, not exhaustive enterprise metadata.
- Explicitly reject 'Single Source of Truth' initiatives in favor of authoritative, domain-scoped sources.
Govern AI Data Consumption
- Approve data sources and consumption patterns for AI agents, analytics solutions, and automation workflows.
- Enforce read-only AI access to systems of record; prohibit AI write-back to ERP or transactional systems.
- Approve material schema changes to datasets consumed by AI, analytics, or reporting.
- Recommend retirement of unused, duplicative, or low-value datasets and reporting assets.
Own Data Lake / Lakehouse Selection and Implementation
- Lead selection of ModusLink's future data lake / lakehouse platform (e.g., Microsoft Fabric, Databricks, Snowflake, Synapse).
- Define business requirements, evaluation criteria, and total cost of ownership analysis.
- Own vendor evaluation, RFP process, and technology recommendation to the executive team.
- Partner with IT (Krishna) and DBAs on infrastructure deployment; own the business-side implementation roadmap.
- Define the initial dataset scope for the lakehouse starting with the highest-value AI and analytics use cases, not enterprise completeness.
- Ensure the lakehouse becomes the primary AI-ready analytics consumption layer, not a parallel data silo.
- Prevent scope creep, over-engineering, and premature enterprise-wide harmonization.
Support the Oracle Fusion Transition
- Serve as the business-side data continuity authority through Fusion cutover.
- Align definitions and authoritative sources between SAP ECC (current) and Oracle Fusion (future).
- Prevent AI and analytics rework by ensuring stable, well-mapped data through and after the ERP transition.
Partner and Protect
- Act as the trusted liaison between business teams, IT, AI engineering, DBAs, and analytics stakeholders.
- Partner with DBAs on operational safety, naming, lifecycle, and cost guardrails without owning infrastructure.
- Support standardized data access, classification, privacy, and compliance practices aligned with company policy.
- Build a culture of data accountability and responsible AI-driven data use.
What This Role Is NOT
- Not a database architect or hands-on data modeler.
- Not a data engineer or pipeline developer.
- Not a hands-on lakehouse builder this role selects, sponsors, and directs implementation, but does not write the code.
- Not a governance-first, framework-first, or catalog-first role.
Success Metrics (First 3 Months)
- Number of AI/analytics use cases onboarded with trusted, documented datasets.
- Reduction in data definition escalations and rework cycles.
- Schema stability rate for datasets consumed by production AI and analytics.
- Time-to-data for new AI use cases.
- Zero AI-related data incidents involving unauthorized system-of-record modifications.
- Documented Fusion data continuity plan for top 10 domains.
- Data lake / lakehouse platform selected, contracted, and initial workload deployed within 9 months.
Required Qualifications
- Bachelor's degree in Information Management, Business, Finance, Supply Chain, Computer Science, or related field. Equivalent experience may substitute up to 4 years.
- 7 10+ years of progressive experience in enterprise data, analytics, BI, ERP reporting, or business operations leadership.
- Demonstrated ownership of data domains in ERP-heavy environments (SAP, Oracle, or similar).
- Experience surviving at least one ERP migration or major system transition.
- Strong stakeholder management, conflict resolution, and cross-functional influence at the director/VP level.
- Comfort operating with imperfect data, ambiguous ownership, and business trade-offs.
- Governance-light, execution-first mindset.
Preferred Qualifications
- Prior experience in logistics, supply chain, manufacturing, or global distribution.
- Exposure to AI/ML consumers of enterprise data (RAG, agents, Copilot, Power BI copilots).
- Direct experience selecting and implementing at least one modern data lake or lakehouse platform (Microsoft Fabric, Databricks, Snowflake, Synapse) as a business/product owner, not as the hands-on builder.
- Familiarity with medallion (Bronze / Silver / Gold) or similar lakehouse layering patterns.
- Experience with Power BI semantic models.
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