Consultant, Data & Analytics, NA

The Coca-Cola Company•Atlanta, GA

1d•$96,500 - $115,000•Onsite

About The Position

The Coca-Cola Company is seeking a Consultant, Data Analyst (Data Solutions) to support the Data Analytics & Engineering team within the Digital & Data NAOU organization. This role supports the design and delivery of scalable, high-quality data and AI solutions by working closely with business and technical teams. The position helps gather, document, and validate business requirements while partnering with Data Engineering and Analytics Engineering teams to implement governed datasets and data products. The ideal candidate brings strong analytical and problem-solving skills, a solid understanding of data structures, LLM-enabled data solutions, and semantic models, along with hands-on experience building reports and dashboards using curated datasets (e.g., Power BI). This role provides hands-on support for data validation, troubleshooting, and reporting activities, helping ensure solutions are accurate, reliable, and aligned with business needs.

Requirements

Bachelor’s degree in Computer Science, Engineering, Business, Information Systems, Finance, or a related field.
Experience supporting requirements gathering, documentation, and validation for data and analytics solutions.
Hands-on experience building reports and dashboards using Power BI, Tableau, or similar tools.
Experience with data structures, relational data concepts, and semantic data models.
Experience collaborating with business, engineering, and architecture teams.
Experience contributing to the development and testing of AI agents leveraging enterprise data models and Agentic AI frameworks.
Proficiency in SQL and Python with ability to work across large, complex datasets.
Analytical and problem-solving skills with attention to data quality and accuracy.
Familiarity with ETL/ELT processes, data pipelines, and modern data platforms (e.g., Azure, Microsoft Fabric, Databricks).
Familiarity with developing and testing AI agents using agentic AI frameworks and LLMs.
Effective communication skills with the ability to work independently and collaborate across cross-functional teams.

Nice To Haves

Experience working in the CPG domain or beverage sector.
Experience working with data lake, data warehouse, or lakehouse architectures.
Exposure to enterprise data governance and data quality frameworks.
Experience with data validation, testing, or reconciliation processes.
Experience working in data environments with multiple source systems.

Responsibilities

Support the documentation, clarification, and validation of functional and technical requirements while working closely with Data Engineers and Analytics Engineers.
Collaborate with Data Engineers, Architects, and Solution Owners to support the design and delivery of scalable, high-quality data solutions.
Contribute to conceptual and logical data design discussions and help validate that requirements are accurately reflected in data models.
Use SQL, Python, and related tools to perform data exploration, validation, troubleshooting, and reconciliation across complex datasets.
Contribute to developing and testing AI data agents that leverage enterprise data foundations and technologies such as Microsoft Foundry and Microsoft Fabric.
Execute data validation and QA/testing activities across ingestion, transformation, semantic, and reporting layers under the guidance of Solution Owners, documenting findings and defects.
Investigate and support resolution of data quality, integrity, and consistency issues across systems.
Contribute to the development and maintenance of documentation including source-to-target mappings, business rules, data flows, test results, and data lineage.
Build reports and dashboards using curated datasets and existing semantic models while adhering to established KPI definitions and governance standards.
Work closely with Analytics Engineering teams to validate that reporting uses trusted, well-defined data assets.
Support data governance and quality practices, including metadata, data definitions, and access controls.
Conduct descriptive and diagnostic analysis to support data validation, troubleshooting, root-cause analysis, business insights, and product decisions.