Contract Global Data Entry Jobs in Decatur, GA (NOW HIRING)

Key Responsibilities:

• Define GEP's Enterprise Data Architecture

  • Evaluate existing data architecture, platforms, and capabilities across GEP and the broader enterprise ecosystem;identifygaps and opportunities; and define a target-state architecture and modernization roadmap aligned to enterprise standards, business priorities, and future-state digital and AI capabilities.

  • Lead the definition of GEP's enterprise data architecture across equipment telemetry, operational events, service data, product analytics, customer-facing insights, and AI/ML use cases.

  • Establish canonical data models for core entities such as equipment, devices, outlets, customers, bottlers, telemetry events, service events, products, consumption, configurations, and operational states.

  • Define architecture patterns for how data flows across source systems, Unified IoT, KOS, FOS, Microsoft Fabric, Data Hub, APIs, data products, and downstream applications.

  • Partner with GEP Engineering, Platform, Unified IoT, and broader global architecture and technology teams to ensure data architecture standards are implementable, scalable, secure, and operationally sustainable.

  Own Data Contracts and Semantic Consistency

  • Define and govern data contracts between source systems, OEMs, vendors, platform teams, and data consumers.

  • Establish standards for schemas, metadata, lineage, naming conventions, data definitions, and semantic layers.

  • Establish and evolve business ontologies and knowledge models that define the relationships between equipment, telemetry, products, customers, service events, and operational processes, enabling consistent data interpretation, interoperability, discoverability, and AI-ready capabilities.

  • Ensure data generated by equipment and upstream systems can be consistently understood, reused, governed, and consumed across analytics, AI, reporting, and product workflows.

  • Partnerwith Unified IoT and Common Equipment Data Language efforts to ensure telemetry and equipment data are aligned to enterprise-level definitions.

  Enable Data Products at Scale

  • Define architectural standards for reusable data products, including curated datasets, APIs, semantic models, feature layers, and governed access patterns.

  • Support the shift from ad hoc analytics and fragmented reporting toward scalable, reusable data products that answer businessquestionsend to end.

  • Ensure data products are designed for multiple stakeholder personas, including product teams, engineering teams, operations, operating units, bottlers, account teams, and future customer-facing use cases.

  • Partner with Data Product, Analytics, and Data Science teams to ensure data products are production-grade, measurable, documented, and governed.

  StrengthenData Governance and Access Control

  • Define data ownership, stewardship, and governance models across GEP, Operating Units, Bottlers, customers, and external partners.

  • Establish principles for role-based access, domain-based access, data classification, discoverability, and controlled data sharing.

  • Partner with Legal, Security, Privacy, Product, and Engineering teams to ensure data architecture supports regulatory and contractual requirements.

  • Ensure architecture patterns support secure, auditable, and controlled access to sensitive operational, commercial, and equipment data.

  Build AI-Ready Data Foundations

  • Define architecture patterns that make GEP data usable for predictive maintenance, beverage quality intelligence, operational optimization, product analytics, andadditionalAI-driven applications.

  • Partner with Data Science and AI teams to standardize reusable features, semantic models, and data products that accelerate model development and deployment.

  • Ensure enterprise data powering AI and analytics initiatives is reliable, traceable, semantically aligned, and governed through robust data architecture, ontology/semantic modeling, lineage, metadata management, to enable trusted AI and measurable business outcomes.

  • Help reduce duplication of feature engineering, one-off modeling datasets, and fragmented analytical logic.

  Partner Across the GEP Ecosystem

  • Work closely with Unified IoT product leadership, platform engineering, product teams, data engineering, data science, experience design, and equipment platform teams.

  • Serve as the data architecture point of contact for strategic initiatives involving KOS, Unified IoT, Common Equipment Data Language, Fabric, Data Hub, predictive maintenance, beverage quality, and customer-facing analytics.

  • Influence architecture decisions across internal and external stakeholders without relying solely on direct reporting authority.

  • Provide technical leadership and architecture guidance to vendors and delivery partners, ensuring vendors execute against GEP-defined standards rather than defining architecture independently.

• Qualifications & Requirements:

• • 10+ years in data architecture, enterprise data platforms, data engineering, analytics architecture, or related technical leadership roles.

  • Experience defining data architecture across complex, multi-system enterprise environments.

  • Strong experience with cloud data platforms,lakehouseor warehouse architectures, data modeling, metadata, semantic layers, APIs, and data integration patterns.

  • Experience designing or governing data products, data contracts, canonical data models, or enterprise semantic layers.

  • Experience working across data engineering, product, software engineering, analytics, and business stakeholders.

  • Strong understanding of data governance, quality, lineage, access control, classification, and stewardship.

  • Ability to translate complex business needs into

• Preferred Skills:

• • Experience with IoT, telemetry, connected devices, manufacturing, operational technology, industrial systems, or equipment data.

  • Experience with Microsoft Fabric, Azure data services, Databricks, Snowflake,orother modern enterprise data platforms.

  • Experience designing architectures that support AI/ML, predictive analytics, operational intelligence, or real-time monitoring.

  • Experience with data mesh, domain-oriented architecture, federated governance, or distributed data ownership models.

  • Experience working with external partners, OEMs, vendors, bottlers, franchise networks, or customer-facing data ecosystems.

  • Experience with metadata management, data catalogs, data discovery tools, observability, and data qualityframewor