Manager of Data & Analytics

The Manager of Data & Analytics will serve as the senior leader accountable for ISCO's enterprise data strategy, governance program, analytics capabilities, and AI/ML roadmap. This role owns the end-to-end data value chain - ensuring data is governed, trustworthy, accessible, and actively leveraged to drive operational excellence, strategic decision-making, and competitive advantage.

ISCO is at the early stages of its data maturity journey. The Manager will be expected to stand up foundational governance and data quality capabilities while simultaneously charting the longer-term vision for analytics, AI, and data-driven transformation. This requires a leader who can operate at both the strategic and tactical levels - someone who can present a data strategy to the executive team and also roll up their sleeves to define metadata standards, select tooling, and work through data quality issues on the plant floor.

As a midsize organization, ISCO requires this leader to combine the strategic oversight of a data executive with the hands-on capabilities of a governance architect and lead data steward, particularly in the program's early phases. As the team and program mature, the Manager will shift increasingly toward strategy, stakeholder management, and organizational leadership.

Scope of Accountability

The Manager of Data & Analytics has enterprise-wide accountability spanning:

• Enterprise Data Strategy: Setting the vision, roadmap, and investment priorities for data, analytics, and AI across ISCO.
• Data Governance Program: Owning the governance operating model, policy framework, stewardship network, and metadata standards across all priority domains.
• Master Data Domains: Product, Customer, Supplier, Item/Material, Facilities/Fleet, and Quote data.
• Operational & Manufacturing Data: Fabrication, labor tracking, work orders, Bills of Materials (BOMs), quality management data (QMDs), and OT/IT integration.
• Analytics & AI: Business intelligence, advanced analytics, predictive modeling, and AI/ML initiatives enterprise-wide.
• Cross-Functional Data Integration: Data flowing across operations, sales, quality, finance, and manufacturing systems (ERP, Pipeline, Excel, fabrication systems).
• Team & Capability Building: The Data & Analytics function including data engineers, analysts, stewards, architects, and data scientists.

Key Responsibilities

1. Enterprise Data Strategy & Vision

• Define and own ISCO's enterprise data strategy, aligning data investments with business objectives, the Target Operating Model, and the company's multiyear transformation roadmap.
• Establish a clear, prioritized, and funded multi-year roadmap for data governance, architecture, analytics, and AI - with measurable milestones and business outcomes.
• Serve as the executive voice for data across the organization - articulating the value of data to the leadership team and building enterprise-wide commitment to data-driven decision-making.
• Identify and evaluate emerging technologies, methodologies, and industry trends (e.g., data mesh, data products, generative AI) for applicability to ISCO's context.
• Develop business cases and ROI frameworks for data investments, ensuring initiatives are tied to measurable value creation.

1. Establish and Lead ISCO's Enterprise Data Governance Program

• Launch and mature foundational governance capabilities including:




• Identifying authoritative "single source of truth" domains.
• Establishing a data ownership and stewardship model.
• Implementing data quality controls and a quality framework.
• Defining governance roles, processes, metadata requirements, and Critical Data Element (CDE) selection.




• Stand up enterprise-wide policies for data lineage, definitions, data ethics, privacy, security, retention, and lifecycle oversight.
• Introduce a structured governance operating model spanning Product, Customer, Supplier, Facilities/Fleet, and other critical domains.
• Develop and maintain a governance policy library, including clear procedures for policy creation, interpretation, enactment, and exception handling.
• Establish and chair (or co-chair) an enterprise Data & Analytics Governance Board, setting cadence, membership, decision-rights, and escalation paths.

1. Design and Manage Metadata Frameworks & Knowledge Organization

• Create and maintain a categorization framework for data assets - including taxonomies, ontologies, business glossaries, and controlled vocabularies - to maximize accessibility and reusability across the enterprise.
• Structure business metadata in a logical and coherent manner, establishing procedures for updating and modifying definitions and information models in a controlled way.
• Set standards for the onboarding and linking of technical data assets to business metadata using metadata management solutions.
• Ensure alignment between business concepts, data models, and technical assets so that information retrieval and data sharing are consistent and reliable.
• As the team grows, transition hands-on metadata architecture work to a dedicated Governance Architect while retaining strategic oversight and quality assurance of the framework.

1. Coordinate and Lead Data Stewardship Activities

• Build and lead the enterprise stewardship network, establishing standard processes for how stewards execute their activities (work steps, tools, communication cadences).
• Mentor and guide data stewards in stewardship activities including data quality remediation, metadata capture, and business definition maintenance.
• Interpret governance policies and translate them into actionable guidance for stewards and business users.
• Provide consolidated reporting on stewardship activities, data quality status, and policy compliance to the governance board and executive leadership.
• As the program matures, recruit and develop a Lead Data Steward to assume day-to-day stewardship coordination while retaining program-level accountability.

1. Mature Data Quality & Master Data Management (MDM)

• Lead MDM/MDG initiatives to improve consistency of product, customer, item/material, quote, and facility data.
• Address systemic data quality issues identified in operations and manufacturing, such as:




• Inconsistent data entry causing manual cleanup and undermining repeatability.
• Fragmented data sources causing discrepancies in labor hours and planning decisions.
• Lack of accurate labor tracking impacting variance analysis and costing.




• Drive implementation of enterprise-grade Data Catalog & Data Quality tools (such as Collibra, Alation, Informatica, Atlan, Monte Carlo, Soda) for metadata management and automated quality monitoring.
• Establish a continuous improvement model for data quality - moving from reactive cleanup to proactive prevention through root-cause analysis, process redesign, and automated controls.

1. Enable Modern Data Architecture & Data Integration

• Own the design and evolution of ISCO's enterprise data architecture, ensuring scalable, reliable data systems that align business strategy with IT architecture and future ERP.
• Identify and prioritize data integration needs across operations, sales, quality, and finance.
• Drive harmonization of data sources (ERP, Pipeline, Excel, QMD, fabrication systems, etc.) to reduce manual reconciliation and improve accuracy.
• Provide data architecture leadership for ISCO's ERP modernization initiative, ensuring governance, quality, and integration requirements are embedded in the program from the outset.
• Evaluate and guide architectural decisions around cloud data platforms, data lakehouse patterns, real-time streaming, and API-based integration.

1. Build and Lead Analytics, BI, and AI Capabilities

• Own the enterprise analytics and AI roadmap, including forecasting, predictive quality, anomaly detection, SKU/production optimization, and operational intelligence.
• Drive modernization of ISCO's BI environment - establishing self-service analytics capabilities, standardized reporting frameworks, and governed data products that business users can trust.
• Lead real-time manufacturing reporting and alerting through integrated OT/IT data (QMDs, fabrication, work orders).
• Drive AI/ML initiatives aligned to business needs such as:




• Demand forecasting and inventory optimization
• Predictive maintenance and predictive quality
• Automated process efficiencies
• Sales/Customer analytics and digital experiences
• Digital twin and simulation capabilities




• Establish an AI governance framework - including model validation, bias monitoring, explainability standards, and responsible AI practices - ensuring AI initiatives are trustworthy and aligned with organizational values.
• Identify and execute quick-win analytics projects that demonstrate value early and build organizational appetite for advanced capabilities.

1. Organizational Leadership & Team Building

• Build, lead, and develop a high-performing Data & Analytics function, including data engineers, analysts, data stewards, governance architects, and (over time) data scientists.
• Define the organizational structure, hiring plan, and capability development roadmap for the D&A team - aligning headcount and skills to the multi-year strategy.
• Establish a culture of data literacy and data-driven decision-making across the enterprise - through training programs, communications, community-of-practice models, and executive engagement.
• Develop and manage the D&A budget, including staffing, tooling, infrastructure, and consulting/contractor spend.
• Create transparent, repeatable processes for ideation, prioritization, intake, delivery, testing, change management, and ROI measurement for all D&A initiatives.
• Ensure transparency, alignment, and proactive communication in data initiatives - addressing gaps noted in IT's current state (unclear prioritization, lack of strategic direction, inconsistent communication).

1. Vendor & Partner Management

• Own vendor relationships for data governance, quality, catalog, analytics, and AI tooling - including evaluation, selection, contract negotiation, and ongoing performance management.
• Manage relationships with consulting partners, implementation firms, and contract resources supporting the D&A program.
• Stay current with the vendor landscape and evaluate platform consolidation or expansion opportunities as ISCO's needs evolve.

Key Relationships & Interfaces

This is a highly visible, cross-functional leadership role requiring strong executive presence and collaborative skills.

• CIO / VP Technology: Direct report. Partners on technology strategy, budget, and organizational alignment. Co-owns the IT-data intersection including ERP modernization and infrastructure.
• Executive Leadership Team: Presents data strategy, business cases, and program outcomes. Advocates for data investment and builds executive commitment to data-driven transformation.
• Data & Analytics Governance Board: Chairs or co-chairs the board. Sets agenda, decision-rights, and escalation paths. Drives policy creation and strategic prioritization.
• Data Stewards (across domains): Provides guidance on standard approaches, mentors on stewardship best practices, ensures consistency across the steward network, and coordinates cross-domain initiatives.
• Subject Matter Experts (Operations, Manufacturing, Sales, Finance): Engages as partners in defining business rules, data definitions, and domain-specific quality requirements. Leverages SMEs as final arbiters on decisions that cannot be resolved through standard governance channels.
• IT & Data Engineering: Partners on technical implementation of data catalog, quality tooling, metadata integration, data architecture, and ERP modernization. Provides architectural direction and ensures alignment between data platforms and governance standards.
• Business Unit Leaders: Aligns governance and analytics priorities with business outcomes; builds trust, adoption, and demand for data capabilities.
• External Vendors & Partners: Manages tooling vendors, consulting firms, and contract resources.

Tools & Technology

The Manager will evaluate, select, and drive adoption of tools across the following categories:

• Data Catalog & Metadata Management: e.g., Collibra, Alation, Atlan, Informatica - for managing business glossaries, data lineage, metadata linking, and asset categorization.
• Data Quality & Observability: e.g., Monte Carlo, Soda, Great Expectations, Informatica Data Quality - for automated quality monitoring, rule enforcement, and incident tracking.
• BI & Analytics Platforms: e.g., Power BI, Snowflake, Databricks, Microsoft Fabric - for reporting, dashboards, self-service analytics, and advanced analytics.
• MDM Platforms: As needed to support master data harmonization across ERP and operational systems.
• AI/ML Platforms: e.g., Databricks ML, Azure ML, SageMaker - for model development, deployment, monitoring, and governance.
• Data Integration & Orchestration: e.g., Azure Data Factory, Fivetran, dbt - for ETL/ELT, data pipeline orchestration, and source-system integration.

Qualifications

Required

• 10+ years of progressive experience in data management, governance, analytics, or related disciplines, with at least 3 years in a leadership role managing teams and budgets.
• Demonstrated experience building and leading an enterprise data governance program, including defining governance roles, policies, operating models, and stewardship networks.
• Proven track record of delivering enterprise analytics or BI capabilities that drove measurable business outcomes.
• Experience designing or overseeing metadata frameworks, including business glossaries, taxonomies, or controlled vocabularies.
• Experience coordinating or leading data stewardship activities across multiple domains or business units.
• Background in manufacturing, operations, or supply chain data environments strongly preferred (aligning with ISCO's context around fabrication, labor tracking, BOMs, SKU management, etc.).
• Experience with modern data platforms and BI tools (e.g., Power BI, Snowflake, Databricks, Microsoft Fabric).
• Strong understanding of data integration, metadata management, data architecture, and data management frameworks (DAMA-DMBOK, etc.).
• Experience evaluating, selecting, and implementing data cataloging and data quality tools.
• Executive presence with the ability to present data strategy, business cases, and program outcomes to senior leadership.
• Ability to lead cross-functional teams with strong communication, influence, and change management skills.

Preferred

• Experience leading data strategy through an ERP modernization (IFS, Infor M3, SAP).
• Experience with Microsoft Fabric and the modern Microsoft data stack.
• Hands-on or management experience with AI/ML model development, MLOps, or advanced analytics programs.
• Familiarity with operational real-time data environments (IoT, sensors, fabrication reporting, OT/IT convergence).
• Experience building a D&A team from the ground up in a midsize organization.
• Experience establishing AI governance frameworks (model validation, bias, explainability).

Skills & Competencies

• Strategic thinking with the ability to translate business objectives into a data and analytics roadmap with clear priorities and measurable outcomes.
• In-depth knowledge of manufacturing, operations, or supply chain business processes and how data flows through them.
• Budget management and business case development for data and technology investments.
• Facilitation and conflict resolution skills - the ability to mediate differing perspectives and requirements across teams and organizational boundaries.
• Ability to communicate effectively with people at all levels, from plant-floor operators to the executive team.
• Ability to influence peers, business stakeholders, and executives to adopt data governance practices and invest in data capabilities.
• Strong analytical skills with the ability to translate business knowledge into accessible frameworks, definitions, and standards.
• Solid project and program management skills to guide multiple concurrent initiatives across governance, quality, analytics, and AI.
• Comfort operating in ambiguity - building structure and process in an environment where little formal data management exists today.

Performance Metrics

Will be measured on their ability to advance ISCO's data maturity and deliver business value, including:

• Data Strategy Execution: Progress against the multi-year D&A roadmap - milestones delivered on time, on budget, and with measurable business impact.
• Governance Program Maturity: Progress against a defined governance maturity model (e.g., policy coverage, stewardship network activation, domain onboarding, governance board effectiveness).
• Data Quality Scores: Measurable improvement across relevant dimensions - existence, validity, completeness, consistency, and timeliness - for Critical Data Elements within governed domains.
• Data Catalog Coverage: Percentage of priority data assets cataloged with business definitions, owners, lineage, and quality rules.
• Policy Compliance & Violation Tracking: Counts and trends of policy violations and business rule exceptions within governed domains.
• Stewardship Activity & Engagement: Active steward participation, issue resolution rates, and cadence adherence.
• Analytics Adoption & Impact: Usage and adoption rates of BI/analytics capabilities; number and value of analytics-driven business decisions or process improvements.
• AI/ML Pipeline: Number of AI/ML use cases identified, validated, deployed, and delivering measurable ROI.
• Business Impact: Reduction in manual report cleanup, data reconciliation effort, and operational rework; improvements in forecast accuracy, planning quality, and operational efficiency.
• Team Development: Hiring plan execution, team capability growth, retention, and employee engagement within the D&A function.
• Stakeholder Trust & Satisfaction: Qualitative feedback from business units on data reliability, accessibility, analytics value, and governance responsiveness.

Success in the First 12-24 Months Looks Like

Months 0-6: Assess, Align, and Activate

• Complete a comprehensive assessment of ISCO's current data landscape, pain points, and opportunities.
• Deliver an enterprise data strategy and phased roadmap, approved by executive leadership.
• Establish the Data & Analytics Governance Board with charter, membership, and operating cadence.
• Launch governance in the first 1-2 priority domains with defined owners, stewards, CDEs, and quality baselines.
• Select and begin implementation of Data Catalog and Data Quality tooling.
• Begin building the D&A team (initial hires or contractor-to-hire placements).

Months 6-12: Build, Deliver, and Demonstrate

• A formal governance structure with defined roles, policies, and processes operational across priority domains.
• A functioning Data Catalog with business glossary, automated quality rules, and measurable DQ improvement.
• A metadata framework (taxonomies, definitions, controlled vocabularies) established for priority domains.
• An active stewardship network with defined processes, cadences, and reporting.
• Deliver 2-3 high-impact analytics or reporting wins that demonstrate value to the business.
• Provide data architecture direction for ERP modernization planning.

Months 12-24: Scale, Mature, and Advance

• Governance expanded to additional domains with increasing organizational self-sufficiency.
• Clear "single source of truth" established for key master data domains (customer, product, material, facility).
• Reduced reliance on manual report cleanup through better data quality and integration.
• Real-time operational and fabrication reporting in place for at least one location.
• AI/ML pilot(s) underway or delivered in at least one business-priority use case.
• D&A team in place with clear roles, capabilities, and development plans.
• A strong foundation for continued scaling of analytics, AI, and data-driven decision-making across ISCO.