Data Engineer

• Develop and implement scalable, secure, and interoperable data systems (including data warehouses, data lakes, and real-time processing platforms) tailored for OU Health’s enterprise data analytics platform.
• Define and maintain conceptual, logical, and physical data models to standardize data across clinical, research, and operational systems.
• Ensure models support healthcare-specific requirements (clinical, claims, provider, member, etc.).
• Design and oversee data integration processes (ETL/ELT pipelines) to enable seamless data flow between OU Health and other entities as needed.
• Work closely with clinical, research, and administrative stakeholders to gather requirements and deliver data solutions that meet departmental needs.
• Facilitate effective communication and collaboration among all parties.
• Monitor and optimize database performance, troubleshoot issues, and integrate new technologies to support evolving healthcare analytics needs.
• Implement robust security and backup procedures to protect sensitive patient information and ensure compliance with privacy laws.
• Prepare and maintain system documentation, data dictionaries, and architecture blueprints.
• Provide training and guidance to team members on data management best practices.
• Identify opportunities to enhance data infrastructure, support innovation, and enable advanced analytics for improved patient outcomes and operational efficiency.
• Work closely with clinical staff, IT professionals, data analysts, and researchers to understand data requirements, workflows, and pain points.
• Facilitate regular meetings and workshops to gather input and align data solutions with clinical and operational needs.
• Act as a liaison between technical teams and non-technical stakeholders, translating complex data concepts into actionable insights.
• Ensure that data architecture decisions reflect the priorities of clinicians, administrators, and compliance officers.
• Promote a culture of knowledge sharing by documenting data models, standards, and best practices.
• Provide training and mentorship to team members on data management, security, and governance.
• Lead collaborative efforts to troubleshoot data issues, optimize workflows, and implement new technologies.
• Encourage open communication and feedback to continuously improve data systems and processes.
• Participate in project planning and execution, ensuring that data architecture aligns with project goals, timelines, and resource constraints.
• Coordinate with project managers and other architects to deliver integrated solutions.

Enterprise Data Architect

• 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

• 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

• Define and implement enterprise-wide AI data architecture strategies to enable secure, scalable, and compliant AI/ML adoption across the organization.
• Design canonical data models, metadata frameworks, and pipelines to support AI/ML model development, training, deployment, and monitoring.
• Establish standards for data quality, lineage, master data management (MDM), and “golden record” frameworks to ensure reliable AI outputs.
• Collaborate with data governance, compliance, and security teams to enforce policies for ethical and responsible AI data usage.
• Architect and oversee AI-ready data platforms (cloud, hybrid, and on-prem) to integrate structured, unstructured, and streaming data sources.
• Implement modern data architectures (e.g., data mesh, data fabric, Lakehouse, Apache Iceberg/Delta Lake) to accelerate AI/ML projects.
• Optimize AI/ML workloads on cloud platforms (AWS, Azure, GCP) and manage data pipelines using tools like Kafka, Glue, Spark, and Snowflake.
• Partner with AI engineers, data scientists, and business leaders to align data architecture with financial products, regulatory compliance, and risk management needs.
• Provide architectural leadership in modernization efforts (real-time payments, fraud detection, personalization engines, and regulatory reporting).

AI Data Architect

• 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.

Manager, Data Engineering

• 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

• 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

• Leads the design, development, test, and maintenance of cloud data solutions throughout the end-to-end lifecycle that meets industry, customer, safety, and regulation standards
• Leads review, analysis, and translation of customer requirements into initial design of software data products
• Coordinates the development, maintenance, enhancement and optimization of software data products and functionalities for systems integrations
• Leads activities to develop, document and maintain architectures, requirements, and designs for software data products
• Debugs and leads resolution of issues identified to ensure the reliability and efficiency of software data products
• Leads research and implementation of current and emerging technologies, tools, frameworks, and changes in regulations relevant to software development
• Leads development, establishment, monitoring and improvement of software data processes, tools and key performance metrics that align projects with organizational goals and industry benchmarks
• Leads execution and documentation of software data research and development projects
• Consults on software domains, system-specific issues, processes, and regulations

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