Data Engineer

• Ensure data pipelines comply with security policies and regulatory requirements (e.g., HIPAA, NIST, SOC 2)
• Develop testing plans to ensure quality of implementation, including deployment validation, data quality checks, reconciliation, and repeatable testing of data pipeline changes.
• Determine and perform complex configuration changes to meet business and information security requirements, including Microsoft Fabric workspaces, data pipelines, connections, service principals, secrets, and environment-specific configuration.
• Provide trouble resolution on complex problems and lead implementations for data pipelines, Microsoft Fabric, Azure DevOps, REST API, and source-system integration issues.
• Provide technical support to business and technology associates in risk assessments and implementation of appropriate information security procedures, standards and technologies.
• Represent major upgrades and reconfigurations in change control; design and analyze vendor and platform services meeting business and information security requirements, including governed promotion across development, test, and production environments.
• Provide system, data platform, and security architecture support for information security technologies and data pipeline capabilities.
• Represent infrastructure security support in significant projects and perform the most complex operations and administration tasks.
• Respond to level 3 & 4 change and problem requests without supervision; Lead level 1 & 2 incident recoveries and root cause analysis.
• Coordinate and prepare the reporting of data security events and incidents.
• Perform capacity analysis; recommend and implement capacity increases for data platform performance, scalability, and reliability.
• Serve as the technical escalation for results of preventative maintenance routines; Supervise preventative maintenance; Leads lights on initiatives to consolidate equipment and/or implement business relocations.

Data Engineer – Secure Data Pipeline

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers
• Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume
• Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables)
• Implement data upgrade/migration paths from legacy schemas without breaking production
• Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems
• Establish data validation and integrity checks across the financial data layer
• Collaborate with the Product Manager to translate finance domain requirements into durable schema designs
• Document architectural decisions and data model contracts for downstream consumers

Senior Software Engineer, ERP, Data & Domain

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations
• Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application
• Manage and extend ingestion using Fivetran across a growing set of sources
• Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse
• Partner with product and engineering to turn customer needs into reliable, performant reporting experiences
• Collaborate with application engineers to understand source systems and ensure clean, reliable data capture
• Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise
• Document data models, definitions, and architecture to support a growing team

• Collaborate with teams of IT/BI and non-IT personnel to create, manage, maintain and develop enterprise data framework and pipelines.
• Provide hands-on leadership and feedback on projects and production systems.
• Provide training, guidance, and thought leadership to the Data Engineering team.
• Take a leadership role in large-scale corporate projects such as integrations and major customer impacting system upgrades.
• Expand and optimize data pipeline framework across multiple applications and systems.
• Collaborate with the BI team and business analysts to review requirements and translate to ETL design and implementation.
• Understand ETL technical approaches and designs.
• Provide support for existing data pipeline frameworks.
• Diagnose and troubleshoot problems with existing data structures, ETL processes and logic.
• Take a leadership role in setting organizational data management and architecture strategy.
• Manage projects and workload according to FMB standard technology.

Senior Data Engineer

• Design, build, and optimize robust ETLs using PySpark and DBT to process large-scale customer datasets.
• Define the technical vision for DC data architecture, mentor engineers, and manage external contractors to ensure the team delivers high-quality, practical solutions for current and future needs.
• Partner with product, engineering, and applied science teams to scope work and deliver data solutions that address real-world challenges in customer data quality and product feature requirements.

Staff Software Engineer – DC Tech Lead

• Designing, generating, and validating high-quality artificial member data to support testing, analytics, and system integration.
• Build synthetic data scenarios using tools such as GenRocket, ensuring alignment with complex healthcare business rules, membership processes, and data models.
• Oversee the end-to-end data lifecycle—from EDI intake through transformation, validation, and integration into downstream platforms—ensuring data integrity, accuracy, and usability.
• Collaborate with business and technical partners to deliver reliable, compliant, and test-ready data assets, while identifying opportunities to improve data generation, validation, and integration processes.
• Operate within Humana's enterprise data governance, security, and architectural standards.
• Report to an Associate Director of Technology Leadership.
• Collaborate with teams to design, build, and maintain scalable data solutions with a specialized focus on synthetic member data generation, EDI processing, and data validation.
• Ensure data is accurate, testable, and aligned with enterprise standards for reliability, accessibility, and security.

Data Engineer, Synthetic Data Generation

• Solving our unique data-lake challenges: transforming and seamlessly normalizing highly varied partner datasets (such as donation data);
• Design robust batch-processing pipelines capable of extracting and loading massive datasets from a variety of internal, external, and public sources;
• Developing processes for data mining, data modeling, and data production;
• Leveraging modern technology knowledge to champion evolving industry trends, updated design patterns, and engineering excellence across the team;
• Autonomously scope, design, and execute complex data projects from day one, turning ambiguous requirements into clear, decisive technical plans;
• Developing robust testing and monitoring systems for scheduled processes;
• Collaborating with cross-functional teams to support their data infrastructure needs;
• Joining each and every one of your colleagues in creating an inclusive workspace.

Staff Data Engineer

• Collaborate with other Engineers and Architects to define the enterprise data architecture roadmap.
• Drive the design and optimization of OLTP and OLAP models for scalability, performance, and maintainability.
• Establish standards for data modeling, storage, and integration across systems.
• Partner with other key stakeholders within engineering in developing a comprehensive data and BI strategy aligned with business objectives.
• Define data governance practices, data access control, compliance, and quality standards.
• Lead proof-of-concepts (POCs) for new data technologies and frameworks.
• Own the architectural review process and guidance to development teams on data-related projects.
• Establish and ensure best patterns and practices for ORM usage (Entity Framework and others) and SQL optimization, according to business needs.
• Evaluate and recommend where AI/ML can enhance data-driven decision-making.
• Stay current with modern data engineering trends, cloud-native architectures, and big data platforms.
• Actively participate and take ownership of Data Architecture artifacts via ADR process.
• Drive AI adoption by introducing and guiding teams in leveraging AI capabilities within the data engineering space to enhance internal efficiency, innovation, and decision-making in everyday workflows.
• Liaison between technology and product teams assisting teams in translating business requirements into tangible architectures that meet business needs now in and in the future.
• Design data architectures that accurately reflect and support evolving complex product and business processes and are required by our customer base.
• Lead data architecture initiatives, collaborating with cross functional teams, from concept through implementation, including governance and quality frameworks.
• Identify opportunities for predictive analytics and advanced modeling.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

Data Architect

• Serve as a primary representative of Data Platforms & Engineering within Yum! to executive leadership, building trusted relationships with senior business, technology, and market leaders while influencing enterprise strategy, technology investments, and platform priorities.
• Develop and deliver executive-level communications, business cases, strategic roadmaps, and operational reviews, translating complex technical concepts into clear business outcomes and actionable recommendations for senior leadership and C-level stakeholders.
• Lead a globally distributed organization supporting enterprise data platforms, establishing operating models, execution frameworks, and accountability mechanisms that support Yum!’s AI, Data & Analytics strategy.
• Build high-performing technical teams through coaching, succession planning, and organizational development while driving prioritization across competing business and technology demands.
• Own budget planning and financial stewardship across assigned platforms, tooling, vendor relationships, and delivery functions.
• Own the strategy, roadmap, and continuous evolution of the modern cloud data stack, including Snowflake, dbt Core, Airflow/Astronomer, Monte Carlo, AWS, and related technologies.
• Establish engineering standards across CI/CD, automated testing, modular pipeline design, semantic modeling, and observability.
• Evaluate and introduce emerging technologies, including AI-driven automation, to improve platform scalability, operational efficiency, and speed of delivery.
• Define and execute the long-term vision, roadmap, and operating model for enterprise data platforms in partnership with AIDA leadership.
• Drive platform standardization and modernization efforts across brands, functions, and technology teams while influencing investment decisions and technical priorities.
• Lead strategic vendor partnerships, contract negotiations, and commercial discussions while evaluating build-versus-buy decisions.
• Lead globally distributed teams across Data Architecture, Engineering, and Operations, providing strategic direction, organizational leadership, and technical oversight for enterprise data platforms.
• Establish enterprise architecture patterns, engineering practices, and operational processes that improve platform quality, resiliency, maintainability, and speed of delivery.
• Oversee delivery and support of enterprise data products, platform capabilities, and foundational datasets that power analytics, AI, and operational decision-making.
• Drive continuous improvement through automation, observability, performance optimization, and operational excellence.
• Serve as the senior authority for platform architecture and engineering decisions, guiding complex technical tradeoffs and influencing enterprise technology standards.
• Partner with business, product, technology, and BYTE Engineering leaders to translate strategic priorities into scalable data products and platform capabilities.
• Act as a trusted advisor on platform capabilities, architecture decisions, and data strategy, ensuring alignment between technical investments and business outcomes.
• Drive adoption and measurable value of enterprise data products while ensuring solutions are scalable, reusable, and aligned with broader enterprise technology roadmaps.
• Lead, mentor, and scale global teams by defining talent strategies, organizational structures, and capability development plans that support long-term growth and evolving business needs.

Director, Data Platforms Engineering

• Responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem.
• Own the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization).
• Partners closely with the Market BI team to improve data access and flow.
• Owns and evolves the Wisconsin Medicaid Market's data stores and data movement ecosystem, including the Data Warehouse and ODS.
• Accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency.

Lead Data Engineer – Modernization, Reliability

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Develop and execute the strategic roadmap for integrated data engineering advancement aligned with enterprise objectives.
• Lead and mentor a diverse team of data engineers, architects, and analysts across multiple domains, fostering an inclusive and high-performing culture.
• Oversee the design, implementation, and optimization of data platforms, data warehousing, and enterprise data products.
• Guide the migration, integration, and management of data across cloud and on-premises environments, ensuring data quality, security, and compliance.
• Partner with stakeholders to understand business needs, translating them into scalable, robust data solutions.
• Drive innovation in data mastering, governance, and operational reporting to maximize business value.
• Ensure timely and accurate data movement and submissions for regulatory, operational, and analytic purposes.
• Promote best practices in data architecture, engineering, and agile delivery methodologies.
• Represent Integrated Data Engineering Advancement in executive forums, cross-functional initiatives, and external partnerships as needed.

VP, Integrated Data Engineering

• responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem across two independent health plan technology stacks.
• owns the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization), and improves the reliability, observability, and security posture of data pipelines supporting critical Medicaid operations.
• partners closely with the Market BI team as their IT counterpart to improve data access and flow, including establishing and managing Databricks pipeline patterns and platform enablement as the BI environment evolves.
• accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency in a complex dual-stack environment.
• primary IT partner for data platform enablement — improving data access patterns, strengthening pipeline governance, and helping establish a maintainable approach to Databricks pipelines and workflows as the market matures its analytics capabilities.

• Deliver new technological solutions to meet business needs within a specified scope while aligned to enterprise goals
• Operate across a range of complexity and scope from moderate initiatives to strategic programs
• Translate business strategy into scalable technical solutions
• Focus on building cloud first data solutions, data governance, and data foundations with leading best practices
• Prepare and deliver executive-ready presentations, status updates, and recommendations that guide strategic decision-making
• Gather and document business system and functional requirements
• Influence system design to optimize support
• Ensure solutions meet the business objectives and requirements
• Complete and/or coordinate implementation of design and requirements, testing, operational readiness, and transition to the appropriate production support team
• Advise executives to develop functional strategies (often segment specific) on matters of significance
• Research and incorporate industry-leading cloud practices related to Cloud data solutions, data governance and data foundations (Reference Data Management, Metadata, Data Quality)
• Execute rapid prototyping to validate complex business requirements
• Work with IT to get requirements accurately implemented
• Build UAT testing and post-production validation

Technology Implementation Lead – Finance Data Platform

• Oversee all aspects of data platform operations, including: SDLC adherence, Change management, Financial governance, Vendor coordination, Cloud cost optimization, Technology lifecycle activities
• Collaborate with Engineering, Product, Security, Compliance, and Business teams to ensure technology solutions are delivered on time, within budget, and in alignment with organizational goals

Manager, Data Engineering

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

• Establish and uphold the goals and expectations that support the fulfillment of cultural and business objectives.
• Operate with a team-first mindset, committed to ensuring the overall success of the team and organization.
• In collaboration with the Part D business team and BI engineers, lead data engineering solutions (design, data movement, orchestration, optimization) for prioritized business requests impacting organizational and enterprise initiatives.
• In Databricks: design, build, maintain, and support the data analytics platform, ensuring the platform is optimized for performance and the right tools are being used for the right jobs.
• Work collaboratively with teammates to optimally apply data warehouse, cloud, and BI architecture, normalization, and data visualization techniques to deliver solutions.
• Create and impact data scale solutions at the QCSA/Enterprise level to meet business needs.

Data Engineer jobs in Kentucky

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Data Engineer – Secure Data Pipeline

Senior Software Engineer, ERP, Data & Domain

Data Engineer

Senior Data Engineer

Staff Software Engineer – DC Tech Lead

Data Engineer, Synthetic Data Generation

Staff Data Engineer

Data Architect

Director, Data Platforms Engineering

Lead Data Engineer – Modernization, Reliability

Supply Chain Data Engineer

VP, Integrated Data Engineering

Lead Data Engineer – Modernization, Reliability

Technology Implementation Lead – Finance Data Platform

Manager, Data Engineering

Data Engineer

Lead Data Engineer