Director of Data

Open to Remote - EST/CST Required.
Dallas, Columbus, Cincinnati and Richmond

Think Consulting is a national technology and operations consulting firm partnering with organizations to build the people, process, and technical capability behind their growth. We've been engaged by Our Client - a fast-scaling, multi-operating-company enterprise building an enterprise data capability from the ground up - to identify a Data Director who can take a young data foundation and turn it into a governed, trusted, self-service platform for the business.

In under two years, Our Client has stood up a modern cloud data lake and warehouse, brought a next-generation data platform live, implemented an enterprise financial planning system, and hired its Financial engineering and analytics staff. 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 BI tools instead of queuing for reports.

• Own day-to-day operation and roadmap execution for the core data platform, including planned capacity upgrades.
• Manage the intake, prioritization, and delivery of data engineering and analytics work across Our Client's operating companies, balancing enterprise build-out against operational reporting demand.
• Oversee ingestion and modeling from multiple ERP environments, the corporate financial planning system, the incoming enterprise CRM, and the integration platform - building the common customer, opportunity, and project intelligence layer that sits above the operating-company 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 integration patterns, and eliminate single-threaded knowledge on critical data and integration flows.

• Stand up and operate enterprise data governance, including a data 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.

• Lead the 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.

• 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. Current Team size is 4 people expected to double next year
• 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.

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

Candidates with deep Microsoft-stack experience (Fabric, Power BI, Purview, Microsoft 365) will be especially well-matched, but the core requirement is enterprise data platform and governance depth - not a specific vendor stack.

• Bachelor's degree in computer science, information systems, analytics, engineering, or equivalent practical experience.
• 7+ years in data engineering, analytics, or data platform roles, including at least 2 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 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.
• 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.

• Data quality scorecard published and reported monthly, with named owners for each core domain.
• Data 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 BI adoption extended beyond executive reporting into at least two business functions.
• Platform 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.

• Builds capability rather than dependency - leaves documentation, standards, and trained people behind.
• Comfortable operating in a growing environment with incomplete inputs and competing priorities.
• Bias toward governed, reusable solutions over fast one-off answers, without becoming a bottleneck.
• Credible with both engineers and executives.
• Collaborative across IT pillars - applications, integration, cyber, and AI.

Equal Opportunity Employer, including disability and protected veteran status