Sr. Data Engineer

At The Coca ‑ Cola Company, we believe data is the foundation for creating personalized experiences and powering sustainable growth in today’s digital-first world. As consumers engage with our brands in more connected ways than ever before, we are investing in advanced data and analytics platforms to unlock deeper insights, accelerate decision-making, and enable seamless innovation at scale. In this role as a Sr. Data Engineer , you will play a critical part in shaping how data flows across our enterprise ecosystem. By leveraging your deep expertise in Spark, Scala development, and JVM-based engineering, you will design and implement integration frameworks that power both batch and streaming workloads on Microsoft Fabric and Synapse Analytics.

About Coca-Cola Company

Location(s): Mexico City/Cities: Mexico Travel Required: 00% - 25% Relocation Provided: No Job Posting End Date: October 30, 2026 Our Purpose and Growth Culture: We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference.

We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves.

We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve.

We believe that our culture is one of the reasons our company continues to thrive after 130+ years.

Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.

Responsibilities

• Design and develop data integration pipelines and backend services for structured and unstructured data, using Apache Spark with Scala.
• Build and optimize distributed computing applications that deliver scalable batch and streaming workloads within Microsoft Fabric environments.
• Perform advanced Spark performance tuning, including partitioning strategies, memory configuration, query optimization, and workload balancing across large datasets.
• Develop REST-based microservices and APIs on the JVM to enable seamless interoperability between internal and external systems.
• Engineer modular, testable, and maintainable code applying functional programming principles and industry-best patterns for reliability and performance.
• Optimize workflows through data partitioning strategies, storage formats, and platform configurations to enable high-performance querying and cost efficiency.
• Champion the "You Build It, You Run It" philosophy.
• Conduct deep Spark performance tuning - managing memory, query plans, partitioning strategies, and capacity optimization to deliver highly efficient batch and streaming workloads within Microsoft Fabric.
• Architect and maintain robust CI/CD pipelines using GitHub Actions, Maven, and SBT to automate testing, deployment, and configuration of data artifacts.

Requirements

• Apply strict functional programming and SOLID principles to write clean, modular, and testable Scala code.
• Qualifications & Experience Strong proficiency in Scala programming with demonstrated knowledge of functional programming concepts, concurrency models, and modular system design.
• Spark Expertise ( 3 + years): Deep, hands-on experience building, debugging and optimizing distributed computing applications using Apache Spark (Scala/Java preferred) .
• Experience designing data models (STAR, Vault, Tabular) for analytics.
• Experience building REST based microservices on JVM.
• Synapse/Fabric Experience (3+ years of combined experience); Designing and transforming large data using Azure Data Services (Synapse, ADF, Azure Functions, ARM Templates) .
• Proven ability to build automated release cycles.
• Hands-on expertise with Git, GitHub Actions, and build tools (Maven/SBT, UV are required ).
• Experience in Agile development (Scrum, lean techniques) and collaborating with global engineering teams.

Preferred

• Demonstrated experience working with big data ecosystems such as Hadoop, Delta Lake, or similar technologies within cloud platforms (AWS, Azure, or GCP).