Manager of Enterprise Data and Analytics- ERP, Microsoft Fabric, Azure Synapse, Databricks

Director of Enterprise Data and Analytics- Working Manager- own the day to day of the data team

Company is building an enterprise data capability from the ground up. In under two years the organization has stood up an Azure data lake and warehouse, brought Microsoft Fabric live, implemented Oracle EPM, and hired its Financial engineering and analytics staff. The Director of Enterprise Data & Analytics is the hands-on leader who takes that foundation and turns it into a governed, trusted, self-service data platform that supports a business scaling from $1.1B to $2.5B in revenue across three operating companies. This is a working manager role. You will own day-to-day delivery for the data team - pipelines, models, reports, and data quality - while establishing the standards, governance, and operating rhythm that let the team scale as headcount roughly doubles over the next two fiscal years. Success is measured in trusted data delivered fast: sub-60-second access to governed data, a measurable data quality scorecard, and business users self-serving in Power BI instead of queuing for reports.

Key ResponsibilitiesPlatform & Delivery

• Own day-to-day operation and roadmap execution for the Microsoft Fabric platform, OneLake, and the Azure data lake and warehouse, including the planned capacity upgrade from F64 to F128.
• Manage the intake, prioritization, and delivery of data engineering and analytics work across the three operating companies, balancing enterprise build-out against operational reporting demand.
• Oversee ingestion and modeling from four ERP environments (IFS Cloud, Vista/Viewpoint, Sage Intacct, ComputerEase), Oracle EPM, the incoming enterprise CRM, and the Boomi integration platform - building the common customer, opportunity, and project intelligence layer that sits above the OpCo systems.
• Drive reduction in report-gated decision making by building curated, certified datasets that business users can consume directly.
• Partner with the integration team to retire point-to-point integration debt in favor of reusable Boomi patterns, and eliminate single-threaded knowledge on critical data and integration flows.

Governance & Data Quality

• Stand up and operate enterprise data governance, including a Microsoft Purview catalog and master data management across the customer, vendor, project, and employee domains.
• Define and publish a data quality scorecard with owners, thresholds, and remediation workflow; report on it to IT and business leadership on a regular cadence.
• Establish data ownership and stewardship models with business stakeholders in each operating company, including definitions for core enterprise metrics.
• Operationalize the enterprise data standards agreed by the business - one customer definition, one work order process, one employee record - including duplicate-rate targets and a single customer view across all operating companies.
• Ensure data handling meets security, privacy, and access-control requirements in partnership with the cybersecurity function.
• Build the data estate to withstand sponsor and transaction diligence - auditable lineage, segregation of duties, access controls, and validated revenue-adjacent data suitable for institutional-grade reporting.

Business Intelligence & Enablement

• Lead the Power BI estate - workspace architecture, certified datasets, semantic models, licensing, and content lifecycle - as self-service expands from executive reporting to all business users.
• Partner with Finance, Operations, Sales, and HR to translate business questions into durable analytical products rather than one-off reports.
• Build the enablement program (training, documentation, office hours) that makes self-service adoption real.
• Prepare the data estate to support the AI roadmap, ensuring models and pipelines meet the quality and lineage standards governed AI requires.

Team Leadership & Vendor Management

• Manage, coach, and develop a team spanning data engineering, data architecture, analytics, BI development, and data quality; hire and onboard planned additions as the function scales.
• Manage the data engineering augmentation partner relationship, including scope, throughput, knowledge transfer, and reduction of dependency over time.
• Own capacity planning, sprint or delivery cadence, documentation standards, and on-call or support expectations for data platform services.
• Contribute to annual data budget planning and track spend against the approved investment plan.

Technical Environment

The successful candidate will work in and be expected to develop depth across the following environment:

Layer

Platform / Tooling

Data platform

Microsoft Fabric (F64 today, F128 planned), OneLake

Storage & warehouse

Azure data lake and data warehouse

BI & reporting

Power BI (enterprise, self-service)

Governance & MDM

Microsoft Purview; MDM across customer, vendor, project, employee

Integration

Boomi integration platform; API-based patterns

ERP

IFS Cloud, Vista/Viewpoint, Sage Intacct, ComputerEase (consolidation in progress)

Financial planning

Oracle EPM / CPM

CRM

Enterprise CRM selection in progress (Salesforce / Dynamics 365); incumbent OpCo CRMs include IFS CRM, Vista CRM, HubSpot, North Boundary, BuildOps

Productivity & AI

Microsoft 365, Microsoft Copilot, * Data quality scorecard published and reported monthly, with named owners for each core domain.

• Purview catalog live and master data management operating for at least two of the four core domains.
• Certified, documented datasets in place for finance and operations reporting, with measurable reduction in ad hoc report requests.
• Self-service Power BI adoption extended beyond executive reporting into at least two business functions.
• Fabric capacity upgrade executed without service disruption, with cost and performance monitoring in place.
• Team fully staffed to plan, with documented runbooks and no single-threaded platform knowledge.
• Reduced reliance on the augmentation partner through demonstrated knowledge transfer to internal staff.

Pay: From $160,000.00 per year

• Bachelor’s degree in computer science, information systems, analytics, engineering, or equivalent practical experience.
• Seven or more years in data engineering, analytics, or data platform roles, including at least two years leading people or acting as a formal technical lead.
• Demonstrated ownership of a cloud data platform in production - Microsoft Fabric, Azure Synapse, Databricks, Snowflake, or comparable.
• Strong SQL and dimensional or semantic data modeling skills; working proficiency in Python or a comparable language for pipeline work.
• Hands-on Power BI experience beyond report authoring - semantic models, workspace governance, and performance tuning.
• Practical experience integrating ERP data into an analytical environment, and comfort with the messiness of multi-ERP, multi-entity data - The company currently runs four ERPs undergoing consolidation.
• Track record establishing data governance, data quality, or master data practices where none previously existed.
• Ability to communicate clearly with non-technical executives and to translate business questions into data requirements., * Direct experience with Microsoft Fabric, OneLake, and Microsoft Purview.
• Experience in construction, manufacturing, distribution, industrial services, or a comparable project-based or field-services business.
• Experience supporting a multi-operating-company or acquisitive environment, including post-acquisition data integration.
• Experience serving as the data workstream lead on an enterprise CRM, ERP, or HRIS selection and implementation program.
• Familiarity with Boomi or a comparable iPaaS platform, and with Oracle EPM or another enterprise planning tool.
• Experience preparing a data estate for AI or machine learning use cases, including lineage and quality prerequisites.
• Experience managing a delivery partner or offshore augmentation team.
• Relevant certifications such as Microsoft Fabric Analytics Engineer, Azure Data Engineer, or Power BI Data Analyst.