Director of Data Engineering

Gas South•Gainesville, FL

3d•Hybrid

About The Position

The Director of Data Engineering is a senior IT leader responsible for enabling, scaling, and maturing the organization’s data-driven decision making by delivering trusted, secure, and enterprise-ready data platforms and analytics capabilities. This role owns the strategy, architecture, delivery, and ongoing effectiveness of enterprise data engineering and business intelligence platforms, ensuring data is reliable, governed, and aligned to business priorities. Reporting to the Vice President of IT, this leader partners with executive and business stakeholders to translate enterprise strategy into high-value data products, analytics capabilities, and AI-ready platforms. Accountable for enabling advanced analytics and responsible AI through governed platforms including Microsoft Fabric, Azure Data Services, SQL Server, Power BI, and Copilot. This position combines strategic vision with operational accountability across the data lifecycle, from ingestion and integration through semantic modeling, analytics, and AI enablement, while building and leading a high-performing data engineering and architecture organization.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
7-10+ years of progressive experience in data engineering, analytics platforms, or business intelligence, including senior leadership roles.
Proven experience designing and operating enterprise-scale data platforms supporting governed self-service analytics and advanced use cases.
Strong knowledge of modern data architectures, cloud platforms, relational/analytical databases, and enterprise data lake patterns.
Experience integrating data platforms with complex operational systems (CIS, Salesforce or equivalent CRM, Five9 or equivalent CCaS, Oracle-based systems/ERP, and other line-of-business applications).
Demonstrated ability to partner with executives, influence stakeholders, and lead complex, cross-functional initiatives.
Excellent communication, strategic thinking, and decision-making skills.

Nice To Haves

Master’s degree in a related field.
Deep experience with Microsoft data technologies, including Microsoft Fabric, Azure Data Services, SQL Server, and Power BI.
Experience integrating with Oracle-based ecosystems (for example SOA architectures and data integration technologies such as GoldenGate or Attunity).
Strong grasp of relational concepts across platforms (including T-SQL vs. PL/SQL) to inform modeling, performance, and integration decisions.
Hands-on experience designing and implementing data lakes and medallion architectures for analytics, reporting, and AI use cases.
Familiarity with non-Microsoft data technologies such as Snowflake, Databricks, AWS, or open-source data platforms.
Experience enabling AI-assisted analytics on enterprise data platforms.
Experience helping establish enterprise data standards, governance models, and operating frameworks.

Responsibilities

Enterprise Data and Analytics Strategy
Define and execute the enterprise Data Engineering and Business Intelligence strategy aligned to business priorities and long-term goals.
Own the data platform vision and multi-year roadmap balancing innovation, scalability, reliability, security, and cost efficiency.
Translate enterprise strategy into a prioritized portfolio of data initiatives and data products with measurable outcomes.
Mature enterprise data capabilities and define an operating model that enables governed self-service and distributed analytics while maintaining platform standards.
Platform Architecture and Delivery
Architect, build, and continuously evolve a modern, cloud-native data platform ecosystem supporting reporting, analytics, and AI enablement.
Design and implement enterprise data lake architectures (including medallion bronze/silver/gold patterns) for scalable ingestion, transformation, and analytics.
Oversee enterprise ingestion, integration, storage, and processing across structured and unstructured sources, ensuring scalable, reusable, and secure pipelines.
Enable governed self-service analytics via semantic models, standardized metrics, and enterprise BI (including Power BI).
Lead data engineers and architects to deliver reusable, secure data solutions and patterns across the enterprise.
Ensure integration between data platforms and core enterprise systems (CIS, CRM such as Salesforce or equivalents, contact center platforms such as Five9 or equivalents, Oracle-based financial/operational systems, and other line-of-business applications).
Establish and evolve canonical data models and shared business entities to drive reuse and trusted analytics.
Data Products, Value, and Outcomes
Own the lifecycle, prioritization, and delivery of enterprise data products and analytics capabilities.
Ensure investments deliver measurable business value by defining success metrics with stakeholders and tracking value realization.
Operational Excellence and DataOps
Accountable for reliability, availability, performance, scalability, and cost of enterprise data platforms; ensure platforms meet defined SLAs/SLOs.
Establish and mature DataOps (CI/CD for pipelines, monitoring/observability, incident management, and continuous improvement).
Governance, Security, and Compliance
Establish and enforce governance (data quality, lineage, access controls, and lifecycle management) with clear ownership and remediation processes.
Ensure compliance with security, privacy, and regulatory requirements in partnership with Security and Legal teams.
Champion responsible data and AI practices aligned with enterprise policies and ethical standards.
AI Enablement
Enable and guide responsible AI/ML usage on enterprise data and analytics platforms in alignment with enterprise AI strategy and governance.
Partner with enterprise architecture, application teams, and business stakeholders to scale AI-enabled analytics safely and effectively.
Cross-Functional Collaboration
Partner with software engineering to integrate scalable data back-end architectures supporting applications and operational systems.
Collaborate with cloud/infrastructure and business analytics teams to ensure platforms are resilient, secure, cost-effective, and aligned to enterprise reporting and decision-making needs.
Financial and Vendor Management
Own the data and analytics technology budget (forecasting, investment prioritization, and cost optimization).
Evaluate, select, and manage vendors/platform partners; optimize cloud and platform spend to ensure performance, value, and strategic alignment.
Leadership and Talent Development
Build, lead, and develop a high-performing data engineering and architecture organization.
Set clear goals and performance expectations; develop career paths and succession plans.
Foster a culture of accountability, collaboration, innovation, and continuous learning.
Enterprise Influence and Change Leadership
Lead change to drive adoption of data-driven decision making and consistent enterprise standards.
Influence senior leaders to align on data priorities, standards, and operating models.
Represent data engineering in executive forums and cross-functional governance bodies.