Role
The Manager of Information Management and Reporting is responsible for the design, development, and ongoing management of the enterprise data warehouse and business intelligence environment. This role leads all data management and reporting initiatives, works closely with business stakeholders to define master data management and BI requirements, and ensures the data platform delivers reliable, timely, and actionable information. The Manager applies current technologies — including AI-assisted development tools and AI-enhanced analytics capabilities — to improve the quality, performance, and value of data solutions across the organization.
Responsibilities
- Lead the architecture, development, and optimization of the enterprise data warehouse, including data modeling, ETL/ELT pipeline design, and dimensional schema development.
- Utilize AI-assisted coding tools (e.g., GitHub Copilot, Cursor) to accelerate pipeline development, generate SQL and Python code, and improve code quality through AI-driven review and testing.
- Apply AI-powered anomaly detection and automated data quality monitoring to proactively identify and resolve data integrity issues within warehouse pipelines.
- Manage and optimize cloud data infrastructure including Snowflake, Azure Data Lake, and Azure Data Factory to support scalable, high-performance analytics workloads.
- Oversee integration middleware (e.g., Boomi) and API-based data ingestion to ensure reliable data flow across enterprise source systems.
- Direct the design, development, and maintenance of BI dashboards, reports, and self-service analytics solutions that meet business requirements.
- Leverage AI-enhanced BI features — such as natural language querying, smart narratives, AI-generated summaries, and intelligent alerting — within platforms like Power BI Copilot or Tableau Einstein to improve end-user insight discovery.
- Use AI-assisted tools to automate routine report generation and accelerate the development of recurring analytics deliverables.
- Partner with business units to define KPIs, reporting standards, and data visualization best practices.
- Maintain semantic layers and data models that ensure consistent, trusted metrics across all reporting surfaces.
- Define and enforce data governance policies covering data quality, lineage, classification, and cataloging across the enterprise data environment.
- Lead master data management initiatives to establish authoritative data sources and reduce duplication and inconsistency across systems.
- Utilize data catalog and governance tools (e.g., Microsoft Purview) to document data assets, ownership, and usage, and explore AI-assisted cataloging features to improve metadata coverage.
- Manage compliance and governance requirements consistent with corporate risk tolerance and applicable data privacy standards.
- Manage IT services partners and managed service providers in fulfilling SLAs consistent with business needs.
- Manage application and infrastructure vendors in accordance with contractual SLAs, performance expectations, and technology roadmaps.
- Evaluate new and emerging data and AI-enabled tooling, conducting proofs of concept to validate productivity and quality improvements before enterprise adoption.
- Support enterprise architecture design needs based on the selected technology stack and application solutions.
- Prepare and manage the departmental budget, including scheduling expenditures, analyzing variances, and initiating corrective action.
Requirements
· Bachelor’s Degree in Computer Science, Information Systems, Data Science, or a related field required.
· Master’s Degree or MBA with a technology focus a plus.
· PPM certification a plus; PMP or Agile/Scrum certification preferred.
· Minimum 10 years of experience managing a technology or data group.
· Demonstrated experience designing and delivering enterprise data warehouse and BI solutions.
· Hands-on experience with cloud data platforms (Snowflake, Azure) and BI tools (Power BI, Tableau, or equivalent).
· Familiarity with AI-assisted development environments and AI-enhanced analytics features within major BI platforms.
· Data & Analytics Platforms:
Snowflake, Azure Data Lake / Data Factory, Azure Synapse Analytics, Power BI, Tableau, or Looker, Microsoft Purview (data catalog/governance), PostgreSQL and relational databases, Boomi / MuleSoft middleware integration
· Engineering, Development & AI Tools:
SQL, Python; dbt and Apache Airflow, AI-assisted development: GitHub Copilot, Cursor, AI-powered BI: Power BI Copilot, Tableau Einstein, Agile / Scrum methodology, Git / DevOps CI/CD pipelines, Microsoft 365 & SharePoint