VP Data Engineering

About Bank OZK

Founded on a legacy of more than 120 years in banking, Bank OZK is much more than just a company. We’re nationally recognized as an industry leader in financial services. That means we combine exceptional service with innovative technologies to deliver smart solutions to our clients across the country. We’re investing in small businesses, fueling economies in local communities and changing skylines in the largest cities across America. Here, we're not simply filling roles. We're fostering even greater careers.

The foundation for a great career starts with an exceptional team and a comprehensive benefits package. We believe in providing our dedicated team members with the best resources to support their physical, mental and financial wellbeing, including generous PTO, 401(k) matching, health, dental, vision (and pet!) insurance as well as special perks and discounts. Learn more about Bank OZK benefits.

Job Purpose & Scope

Responsible for leading, developing and maintaining data solutions to support Bank Data Strategy. Collaborates with business leaders and Data Engineering teams for timely and reliable delivery of data solutions. Develop technological specifications from defined or identified business requirements.

Essential Job Functions

• Manages staff to include staffing, assigning and delegating work, training, mentoring, and coaching. Conduct performance reviews and foster a culture of accountability, collaboration, and continuous improvement.
• Leads the development and maintenance of data model-based solutions as specified by internal engineering standards. Conducts code reviews and enforces the Bank’s coding standards.
• Provides level of effort data development estimates while proactively managing and communicating scope or delivery changes.
• Architects, designs, and implements scalable and resilient data pipelines and data engineering products using modern cloud-based data platforms, ETL frameworks, programming languages, and transformation tools.
• Provides leadership and continual improvements to data model architecture and technical data delivery practices.
• Develops data model structures, extract transform & load (ETL) services, reporting services, and enterprise data management strategies.
• Owns the full data development lifecycle delivery, including requirements gathering, design, development, testing, deployment, and monitoring.
• Plans and performs unit testing of data solutions to ensure developed products meet business requirements.
• Proactively resolves bugs and data issues as identified by product management, project management, or governance functions.
• Assesses functional requirements in a timely and efficient manner to create and maintain data solutions which fit within data engineering architecture patterns and standards.
• Keeps apprised of technological innovation and make suggestions to management to improve processes and support governance.
• Provides technical, functional, and overall leadership across related functions and offers guidance and mentorship to junior data engineers.
• Ensures compliance with internal and regulatory policies, procedures, and guidelines.
• Performs other duties as assigned.

Knowledge, Skills & Abilities

• Comprehensive knowledge of data modeling (Star/Snowflake schemas), ELT/ETL patterns, and performance optimization in cloud data warehouses.
• Comprehensive knowledge of data quality control, error handling, and data profiling practices.
• Comprehensive knowledge of the Data Engineering Product Development lifecycle.
• Ability to manage and mentor junior staff members.
• Ability to build scalable, modular, and testable data pipelines using Snowflake, dbt, and Python.
• Ability to develop data products with a focus on reusability, scalability, and user-centric design.
• Ability to collaborate cross-functionally, especially with analytics, business, and IT teams.
• Ability to exercise discretion and sound judgment in decision making.
• Ability to manage expectations, prioritize work, and negotiate trade-offs.
• Ability to meet stringent deadlines with accuracy and maintain attention to detail.
• Ability to manage multiple priorities and demonstrate flexibility in a continually changing environment.
• Ability to troubleshoot issues on standard, sensitive, and/or complex accounts.
• Ability to communicate effectively both verbally and in writing and interact with all levels of employees.
• Ability to work independently without close supervision.
• Strong problem-solving, organizational, critical thinking, and time management skills.
• Skill in using computer and Microsoft Office, including Word, Excel, and Outlook.

Basic Qualifications

• Bachelor’s degree in business, computer or data science, data or business analytics, or other job-related fields, or commensurate work experience, required. Relevant master’s degree preferred.
• 5+ years of experience in database development, data warehouse development, reporting, and/or data science experience.
• 2+ years of experience in Jira, Azure DevOps, GitHub or similar code development management environment, required.
• 2+ years of experience with Snowflake, Talend, Python, SQL and other modern data tech stack required.
• 1+ year of experience managing, leading, or mentoring others preferred. Experience leading data and engineering teams preferred.

Preferred Qualifications

• Experience working in banking or finance environments preferred.
• Data development certification such as Snow Pro Certification or Microsoft SQL Server certification preferred.

Job Expectations

Operate customary equipment and technology used in a business environment, with or without accommodation.

Note: This description is not an exhaustive list of all job functions, duties, skills, and job standards required. Other job functions, duties, skills, and standards may be added. Management reserves the right to add or change the job requirements at any time.

Key skills/competency

• Data Engineering Leadership
• Data Strategy
• Data Modeling
• ETL/ELT
• Cloud Data Warehousing
• Snowflake
• Python
• SQL
• Team Management
• Data Pipeline Architecture