Director, Data Strategy

Smile Doctors•Dallas, TX

About The Position

The Director, Data Strategy shapes and executes the company’s data strategy, oversees business intelligence development, enables analytics across departments, and drives data governance, data quality, and modernization. The role leads a team of BI developers, database engineers, and analysts, and owns the New Modernized Data Platform as it moves from build and go-live into steady-state operation. The Director owns the enterprise data and analytics roadmap and the end-to-end data lifecycle, including ingestion, transformation, analytics, and consumption on a cloud data warehouse using a medallion architecture. The role provides architectural guidance across platforms, pipelines, semantic models, and reporting; ensures reliability, monitoring, documentation, cost management, and access governance; and leads governance, security, compliance, and data quality initiatives. The position enables self-service analytics and advanced insights, oversees data products and reporting solutions, and builds a curated, governed data foundation for AI-assisted analytics. It identifies and delivers AI, machine learning, and advanced analytics use cases, evaluates AI capabilities in data and BI platforms, and establishes responsible AI practices. The Director also modernizes legacy systems, partners with IT and business leaders on priorities, communicates progress and risks, and contributes hands-on through code review, data validation, troubleshooting, and report development as needed.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field.
Ten years of experience in data, analytics, or BI leadership, including leading a data platform program.
Three years of supervisory experience, including managing teams of employees and contractors.
Hands-on experience with SQL and Snowflake or an equivalent cloud data warehouse.
Experience delivering a modern data platform end to end, including ingestion, transformation, semantic layer, and reporting.
Experience managing consulting partners and contractors, including statement of work oversight and knowledge transfer.
Experience establishing data quality practices and defining enterprise KPIs and metric definitions.
Advanced knowledge of data architecture, governance, and analytics enablement.
Advanced knowledge of cloud data platforms such as Snowflake, Azure, or AWS, and SQL.
Working knowledge of data warehouse design, including medallion architecture; data orchestration and transformation tools such as Azure Data Factory, SQLMesh, or dbt; data catalog tools such as Atlan; and Power BI.
Knowledge of data quality practices, including profiling, validation rules, monitoring, and issue management.
Strong analytical and problem-solving skills.
Strong written and oral communication skills.
Ability to present complex technical information clearly and concisely to non-technical partners or stakeholders.
Executive presence, including the ability to present confidently and concisely to senior leaders, influence without relying on title, push back respectfully, and secure alignment on priorities.

Nice To Haves

Experience delivering AI or machine learning use cases on a cloud data platform.
Experience in multi-location healthcare, dental, or orthodontic organizations.

Responsibilities

Define and execute an enterprise-wide data and analytics strategy and multi-year roadmap aligned with business goals.
Own delivery of the New Modernized Data Platform through go-live and steady-state operation, including scope, schedule, budget, risks, and dependencies.
Manage consulting partners and contractors and lead knowledge transfers so the internal team can own, support, and evolve the platform.
Own the end-to-end data lifecycle, including ingestion, transformation, analytics, and consumption, using a medallion architecture on a cloud data warehouse.
Provide architectural guidance for data platforms, pipelines, semantic models, and reporting solutions, and review technical deliverables for scalability, performance, and maintainability.
Ensure reliability, monitoring, and documentation of data pipelines and data movement across systems; own cloud data platform cost and access governance.
Lead data governance, security, and compliance initiatives, including enterprise KPI and metric definitions, data catalog, lineage, and stewardship.
Establish and lead an enterprise data quality framework, including standards, automated validation and monitoring, scorecards, and resolution of issues at the source.
Enable self-service analytics and advanced insights for business teams through certified datasets and governed semantic models.
Oversee development, rationalization, and modernization of data products, dashboards, and analytical solutions.
Build an AI-ready data foundation of curated, well-documented, and governed data and metadata to support AI-assisted analytics and natural-language access to data.
Identify, prioritize, and deliver AI, machine learning, and advanced analytics use cases, including forecasting and anomaly detection, taking them from pilot to production with measurable value.
Evaluate and adopt AI capabilities within data warehouse and BI platforms, and apply AI to improve data team productivity, including code review, documentation, testing, and pipeline monitoring.
Establish responsible AI practices, including access controls, protection of patient and sensitive data, and validation of AI outputs in alignment with company policies.
Modernize legacy data systems and drive adoption of cloud-based platforms.
Collaborate with IT and business leaders to prioritize and deliver high-value data initiatives, and communicate progress, risks, and decisions needed in clear business language.
Review code, validate data, troubleshoot pipeline issues, and build critical reports as needed.
Use approved AI tools to complete assigned tasks more efficiently and effectively, in alignment with company policies.