Manager of Data & Analytics

About the role

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.

Responsibilities

• 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.

• 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.
  • 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.

• Ensure alignment between business concepts, data models, and technical assets so that information retrieval and data sharing are consistent and reliable.

• 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.
  • Transition hands-on metadata architecture work to a dedicated Governance Architect while retaining strategic oversight and quality assurance of the framework.
  • Cookordination and Lead Data Stewardship Activities

• Develop and Maintain Metadata Frameworks & Knowledge Organization

• 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.

• 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.

• 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.

• 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.

• 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).

• 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.

Qualifications

• Advanced degree in Computer Science, Information Systems, Statistics, or related field.

• Minimum 10 years of relevant experience in data management, analytics, or related fields.

• Proven track record of leading data governance, master data management, and data quality initiatives.

• Experience with data architecture, data integration, and data warehousing.

• Strong understanding of data ethics, privacy, and security principles.

• Excellent communication and collaboration skills, with the ability to articulate complex technical concepts to non-technical stakeholders.

• Ability to work effectively in a fast-paced, dynamic environment and adapt to changing priorities.

• Experience with data visualization tools and business intelligence platforms.

• Knowledge of data science, machine learning, and AI methodologies.

• Experience with data governance frameworks and standards.

• Experience with data catalog and metadata management tools.

Skills

• Data architecture and design.

• Data governance and management.

• Data quality and integrity.

• Data modeling and normalization.

• Data visualization and reporting.

• Machine learning and AI.

• Collaboration and stakeholder management.

• Project management and execution.

• Leadership and team building.

• Vendor management and relationship building.

Benefits

• Competitive salary and benefits package.

• Flexible work schedule and remote work options.

• Professional development opportunities and training.

• Health, dental, and vision insurance.

• Retirement savings plans.

• Employee assistance programs.

Pay

Commensurate with experience.

Schedule

Full-time.