Analytical Data Engineer

Forvis Mazars•Charlotte, NC

About The Position

Forvis Mazars’ Enterprise Data & Analytics organization is building a modern, scalable data ecosystem to transform enterprise data into trusted, usable information. The Analytical Data Engineer will help design, build, and evolve the enterprise data platform in support of the firm’s data strategy. The role develops scalable data pipelines, data models, and reusable data products that turn data from across the firm into reliable foundations for reporting, advanced analytics, and artificial intelligence. Working in a modern data and AI environment, the engineer applies strong data engineering practices and emerging AI capabilities to solve complex data challenges and improve how data is delivered and consumed. The role partners with technology and business teams to deliver enterprise data solutions and contributes to the firm’s transformation into a more data- and AI-driven organization. The position is full-time and is advertised in Atlanta, Indianapolis, Kansas City, Springfield, Charlotte, Raleigh, and Dallas.

Requirements

Three or more years of data engineering experience with modern enterprise data platforms, including the Databricks Intelligence Platform.
Experience developing scalable data pipelines aligned with Medallion architecture.
Advanced Python and SQL development skills.
Knowledge of dimensional modeling and enterprise data modeling principles.
Experience with Unity Catalog, data security, and data quality practices.
Working knowledge of Databricks AI capabilities and data engineering patterns that support AI and machine-learning solutions.
Experience working in Microsoft Azure, Amazon Web Services, or Google Cloud Platform.
Proficiency with GitLab, CI/CD, automated testing, and DevOps practices.
Strong problem-solving and troubleshooting skills.
Strong written and verbal communication skills.
Ability to collaborate effectively with technical teams, business stakeholders, and cross-functional partners.

Nice To Haves

Bachelor’s degree in computer science, information technology, data engineering, engineering, or a related field.
Databricks professional certification.
Data engineering experience within Microsoft Azure.
Experience building reusable enterprise data products that support analytics and AI use cases.
Experience implementing Unity Catalog and governed access controls in an enterprise environment.
Experience developing automated data quality, testing, and deployment processes.
Experience optimizing Databricks workloads for performance and cost.
Experience working with enterprise-scale data models and complex data environments.

Responsibilities

Design, build, and maintain scalable data pipelines using the Databricks Intelligence Platform.
Develop data pipelines aligned with Medallion architecture and enterprise data standards and guidelines.
Design and maintain reusable data products for reporting, advanced analytics, AI, and machine-learning solutions.
Implement data quality, monitoring, reconciliation, and error-handling processes.
Optimize data workloads for performance, scalability, reliability, and cost.
Assist with implementing data governance and security controls using Unity Catalog.
Apply role-based and attribute-based access controls to support secure enterprise data access.
Develop automated testing and deployment processes using GitLab and CI/CD practices.
Apply dimensional modeling and enterprise data modeling principles to data solutions.
Partner with enterprise architects, data architects, business analysts, data analysts, AI teams, and business stakeholders to deliver enterprise data solutions.
Participate in solution design and code reviews.
Troubleshoot data pipeline, data quality, performance, and platform-related issues.
Document data pipelines, models, technical processes, and solution designs.