Data Engineer

• 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.

Lead Data Engineer – Modernization, Reliability

• 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.

Lead Data Engineer

• Lead the enterprise data architecture and delivery capabilities that power analytics, reporting, and AI-driven initiatives across the organization
• Deliver scalable, secure, and high-quality data services
• Provide production-ready, governed, and scalable data pipelines and platforms
• Drive best practices for solution development within Technology Services
• Work with business leaders to assess corporate data management and identify improvement opportunities
• Implement improvements in data management processes and integrity

Director, Data Engineering – Analytics

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• 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.

• Deploy new data pipelines
• Design & build data observability platforms and metrics
• Build metadata driven pipeline solutions
• Monitor and optimize data transformations
• Build third party data integrations

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

Senior Data Engineer

• Codes software applications based on business requirements
• Designing, developing, and maintaining scalable data pipelines and solutions across cloud and on-premises environments
• Report to the Director of Data Engineering for CenterWell Tech - Primary Care Organization (PCO) Technology Support team
• Work with a development team and all aspects of developing an application and getting this application to production
• Stay informed about relevant technology trends and developments, including new AI-Assisted coding

Data Engineer – EHR Systems

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

Staff Software Engineer, Data Platform

• Design, build, and maintain data architectures and pipelines supporting Application Health reporting and analytics.
• Ingest and integrate data from multiple internal and external sources using modern cloud‑native technologies.
• Develop analytics, dashboards, and scorecards across architecture, data quality, security, engineering standards, reliability, and technology stack alignment.
• Leverage big data and AI/ML‑enabled solutions to cleanse, transform, and enrich enterprise data.
• Provide technical leadership, set engineering standards, review designs, and mentor team members.
• Partner with Technology Stewardship, Tech Strategy, EIP, CIO Segment Engineering, and application owners to align solutions with enterprise priorities.
• Ensure data solutions comply with governance, security, privacy, and regulatory standards.
• Evaluate emerging technologies to improve scalability, performance, reliability, and cost efficiency.

• Design the vision and blueprint of an organization’s data framework.
• Design and manage data systems, set policies for how data is stored and accessed, coordinate various data sources within an organization, and integrate new data technologies into existing IT infrastructures.
• Data modeling and design, including software development and database administration.

Data Architect – Data Ecosystems

• is 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.
• owns and evolves the Wisconsin Medicaid Market's data stores and data movement ecosystem, including the Data Warehouse and ODS, and the ETL processes that connect vendor and internal systems.
• 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.
• is also the Market BI team's 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.
• requires collaboration, high accountability, and comfort operating in ambiguity while continuously improving data reliability, security, and outcomes for associates, members, providers, and other stakeholders.

• Designs, builds, and maintains data processing architectures and solutions enabling the efficient conversion of structured and unstructured data to insights at enterprise scale.
• Ingests data from internal and external sources utilizing cloud native platforms and software development best practices and patterns.
• Develops software tools that leverage artificial intelligence, machine learning, and big-data techniques to cleanse, organize, and transform data into insights and actions that enables Humana to better serve our members.
• Act as one of the primary point of contact for the team, build POCs, build quick prototypes with latest industry standard software libraries and scale it for the platform
• Design, code, test, debug and document programs using Agile development practices
• Develops new documentation and departmental technical procedures
• Makes decisions on moderately complex to complex issues regarding technical approach for project components, and work is performed without direction.
• Exercises considerable latitude in determining objectives and approaches to assignments.
• Troubleshoots business and production issues by gathering information; engaging support teams to assist in the resolution of issues; formulating an action plan; performing actions as designated in the plan; interpreting the results to determine further action; performing root cause analysis to prevent future occurrence of issues; and completing online documentation.
• Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community.

• Play a critical role in building the Agentic AI platform and delivering cutting-edge AI-powered solutions.
• Work with large-scale datasets and state-of-the-art tools to address complex business problems.
• Close collaboration with data scientists and fellow engineers across the entire AI development lifecycle—from data ingestion to deployment and monitoring.
• Explore and implement generative AI technologies using Large Language Models and other generative models to create innovative solutions.
• Build and maintain robust APIs for enabling Retrieval-Augmented Generation and generative AI agents within business use cases.
• Design, develop, and maintain robust data pipelines for ingestion, processing, and transformation of large datasets.
• Assist our data science team in the development and deployment of traditional machine learning models.
• Monitor and maintain performance and reliability of deployed APIs, models, and data pipelines.
• Stay up to date with the latest advancements in machine learning, generative AI, and related technologies.

Senior Data Engineer – AI

• The Data Warehouse Engineer is a hands-on role responsible for designing, building, and operating Maverick’s Azure data platform.
• Reporting to the Data Team Director, you will partner with external implementation resources in Phase 1 and then own daily operations, reliability, and continuous improvement of the platform.
• Monitor health and SLAs via Azure Monitor/Log Analytics, Synapse and Data Factory dashboards; respond to critical pipeline incidents within 2 hours.
• Triage and resolve data integration failures (sponsor bank files, CRM/ITSM connectors, API ingestions), distinguishing data quality vs. pipeline vs. infrastructure issues; escalate when needed.
• Perform root-cause analysis (RCA) and drive corrective actions; maintain and conduct post-mortems.
• Design and build Azure Data Factory pipelines (ETL/ELT) for batch and incremental ingestion using parameterized, reusable templates.

Data Warehouse Engineer

• Joining each and every one of your colleagues in creating an inclusive workspace;
• Owning, maintaining, and supporting the reliability, architecture, and evolution of our production machine learning and delivery platform;
• Designing, operating, and improving large scale Databricks/Spark pipelines and workflows
• Partner with our data scientists to productionize new data and ML-powered products;
• Leading improvements on the software integration layers between data pipelines and product workflows;
• Growing the team via mentorship and leadership, reviewing technical documents, improving code quality, and improving team performance and processes;
• Acting as a senior technical owner in debugging complex production issues and improving system observability;
• Collaborating with cross-functional teams to support their data infrastructure needs.

See all jobs on JobTailor

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

Director, Data Engineering – Analytics

Manager, Data Engineering

Data Engineer

Senior Data Engineer

Data Engineer – EHR Systems

Staff Software Engineer, Data Platform

Lead Data Engineer

Data Architect – Data Ecosystems

Lead Data Engineer – Modernization, Reliability

Senior Data Engineer

Senior Data Engineer – AI

Data Warehouse Engineer

Senior Software Engineer, Data Platform