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We are seeking an individual who thrives in ambiguity, capable of driving data and analytics initiatives with precision and purpose. You will support the implementation of strategies, systems, and processes that enable data-driven decision-making across procurement and supply chain. Reporting to the Source-to-Contract (S2C) Technology Enablement Lead, your responsibilities will include designing the analytics strategy, building pipelines, integrating platforms, enabling visualization capabilities, leveraging GenAI tools, and delivering actionable insights that inform strategic sourcing and procurement decisions.
Job Responsibility:
Develop and implement enhancements aligned with broader S2C strategic objectives and enterprise data strategy
Define and manage a data and analytics roadmap, prioritizing dashboards, AI/ML models, APIs, and data products
Track value delivery through KPIs such as cycle-time reduction, supplier risk mitigation, savings enablement, and user adoption
Build and manage pipelines into data lakes/lakehouses (e.g., Databricks/Delta), integrating ERP, CLM, SRM, and external data sources
Code in SQL and Python for data modeling, cleansing, and transformations
Implement robust data quality, governance, lineage, and audit controls
Deliver executive-ready dashboards and insights using tools such as Power BI, Tableau, or Looker
Establish visualization standards and KPIs to enable consistent decision-making
Convert complex analyses into clear, compelling narratives for senior stakeholders
Apply GenAI tools for procurement use cases, including contract intelligence, supplier summaries, and clause analysis
Build predictive and prescriptive models for demand forecasting, supplier risk, and cost optimization
Leverage ML frameworks for experimentation, feature management, and human-in-the-loop governance
Own product vision, OKRs, and roadmap aligned to S2C/S2P and enterprise data strategy
Maintain a single, prioritized backlog across pipelines, semantic models, APIs, dashboards, and GenAI
Run discovery and define success metrics (adoption, cycle time, savings, SLOs, DQ)
Treat data as a product: accountable owners, SLAs, data contracts, versioning, and deprecation plans
Govern releases: feature flags, controlled changes, release notes, and user training
Manage vendors and TCO
negotiate SLAs
track license utilization and value realization
Partner with category managers to translate procurement and supply chain problems into data-driven use cases
Support savings tracking, supplier diversity, ESG reporting, and risk analysis with data insights
Improve supplier master data accuracy, classification, and taxonomy
Ensure analytics pipelines comply with SOX, GxP, GDPR, CSRD, and internal audit requirements
Own audit documentation, lineage records, and inspection readiness
Implement access controls, data minimization, and privacy standards
Act as a liaison between business and technical stakeholders to translate requirements into scalable data solutions
Lead training, adoption, and enablement for self-service analytics capabilities
Manage vendor relationships and ensure SLAs are met for analytics platforms
Mentor analysts, engineers, and BI developers on data engineering, visualization, and governance practices
Promote best practices in agile delivery, backlog management, and predictable outcomes
Requirements:
Btech/ MBA/ PHD
Minimum of 10 -12 years of relevant business and functional experience
Data Engineering: Hands-on experience with Databricks/Delta Lakehouse, SQL, Python, PySpark, orchestration tools, and CI/CD
Visualization & Storytelling: Proficiency with Power BI/Tableau/Looker and ability to craft executive-level narratives
GenAI & Analytics: Exposure to GenAI/ML platforms and ability to apply them in procurement/supply chain contexts
Domain Knowledge: Strong understanding of procurement and supply chain processes, supplier master data, and compliance frameworks
Governance & Compliance: Knowledge of SOX, GxP, GDPR, and audit practices
Communication & Influence: Ability to simplify complex analytics for stakeholders across procurement, finance, IT, and executive leadership
Strategic Thinking: Ability to design scalable, future-proof data solutions
Collaboration: Strong cross-functional team collaboration skills, including mentorship and knowledge transfer
Adaptability: Comfort in dynamic environments with shifting priorities
Problem Solving: Structured approach to tackling technical and business problems
Attention to Detail: Precision in handling data quality, governance, and regulatory documentation
Nice to have:
Technical Liaison Experience: Ability to bridge between technical and business stakeholders
Change Management: Familiarity with training and adoption practices for analytics and digital tools
Data Science & AI: Experience with ML experimentation, feature stores, and advanced analytics techniques