Vice President, Data Engineering and Analytics

Acosta, Inc.•Jacksonville, FL

39d•Hybrid

About The Position

The Vice President of Data Engineering and Analytics is a strategic executive leader responsible for shaping and executing the organization's enterprise data vision. This role oversees the full data lifecycle-from acquisition and engineering to advanced analytics and AI-driven solutions-ensuring data is a core enabler of business growth, innovation, and operational excellence. Note: This is a hybrid role based in our office environment in either Jacksonville, FL, Lewisville, TX or Mississauga/Toronto, ON . Candidates will be expected to work as much as 3 days per week onsite depending on proximity to corporate hubs.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
Ten (10) or more years of progressive experience in data engineering, analytics, or related domains, including Five (5) or more years in executive leadership roles.
Proven success in leading enterprise data transformations and delivering measurable business outcomes.
Strong understanding of data governance, security, and compliance frameworks.
Demonstrated ability to influence at all levels and communicate complex data concepts to non-technical stakeholders.
Experience managing budgets, vendor contracts, and cross-functional initiatives.
Cloud platforms (AWS, Azure, GCP)
Data warehousing (e.g., Snowflake, BigQuery)
ETL/ELT tools and orchestration frameworks
Programming languages (Python, SQL, R)
Data visualization (e.g., Tableau, Power BI

Responsibilities

Define and lead the enterprise data strategy aligned with business goals.
Champion a data-driven culture across the organization.
Build, mentor, and scale high-performing teams across data engineering, analytics, and data science.
Foster a collaborative, innovative, and inclusive team environment.
Oversee the design and implementation of scalable, secure, and modern data platforms (e.g., data lakes, warehouses, pipelines).
Ensure high availability, performance, and cost-efficiency of data systems.
Drive the adoption of machine learning, generative AI, and predictive analytics to deliver actionable insights and data products.
Partner with business units to identify high-impact use cases.
Establish and enforce enterprise-wide data governance frameworks, including data stewardship, metadata management, and quality standards.
Ensure compliance with data privacy and regulatory requirements.
Collaborate with executive leadership and cross-functional teams to align data initiatives with strategic priorities.
Translate complex data insights into clear, actionable business recommendations.
Manage relationships with external data service providers, ensuring performance against SLAs and cost-effectiveness.