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

• Define the content architecture strategy across platforms — detection frameworks, parser standards, playbook taxonomy, and dashboard design
• Lead platform-specific content roadmaps (Google SecOps, Sentinel, AWS) aligned to customer coverage targets
• Serve as the senior technical escalation point for all Security Solution Engineer — SIEM/SOAR technical challenges — including parsers, detection logic, SOAR automation, API integrations, and custom scripting
• Partner with the FDE and SE&A teams to translate customer-specific findings into reusable, scalable content
• Evaluate new data sources, integrations, and vendor partnerships for content expansion
• Drive adoption of AI-assisted content generation tooling and establish quality review standards
• Mentor Security Solution Engineers — SIEM/SOAR and contribute to hiring and technical growth of the team

Forward Deployed Data Architect

• 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

• Define and evolve the target data architecture supporting economic data systems.
• Design scalable storage architectures capable of supporting billions of time-series observations and historical revisions.
• Develop metadata schemas supporting dataset discoverability, lineage, and governance.
• Standardize dataset structures and metadata across multiple platforms.
• Implement scalable data processing frameworks capable of supporting growing dataset volumes.
• Improve indexing and metadata strategies that support dataset search and discovery.
• Enable advanced analytical capabilities for researchers and developers.
• Partner with the Application Architect to optimize data access patterns for APIs and applications.

Data Architect

• Apply knowledge of health care and database management to analyze clinical data, and to identify and report trends.
• Design and build modern data-centric software applications to support clinical and operational processes across all parts of the healthcare system.
• Work closely with the Product, Platform, and Architecture teams to deliver on joint efforts.
• Build data pipelines and transformations, data enrichment processes, provisioning layers, and user interfaces to meet the requirements of key initiatives.
• Mentor and assist less experienced Data Engineers and participate in research and development of new technologies to help set up repeatable processes or templates for other Data Engineers to follow.

Senior Data Engineer IS

• Design and deliver scalable data pipelines in AWS and Databricks that drive reliable, production-ready data solutions
• Proactively monitor, troubleshoot, and resolve data pipeline issues to ensure consistent system performance and data integrity
• Triage and resolve incidents based on business impact, ensuring timely and effective issue resolution
• Partner with business stakeholders to investigate and explain data discrepancies, improving trust in data assets
• Develop ad hoc analyses and reports using SQL and Python to support critical business decisions
• Automate manual processes to improve efficiency, reduce errors, and accelerate delivery timelines
• Collaborate across engineering, platform, and product teams to manage dependencies and resolve service issues
• Deliver solutions within an Agile (Kanban/DevOps) environment, contributing to continuous improvement and faster delivery cycles

Software Engineering Advisor – Data Engineer

• Architect Petabyte Pipelines: Engineer scalable, reliable, and performant data pipelines to assemble large and intricate datasets using SQL, DBT, and Snowflake, ensuring high data availability and integrity.
• Build Data Platforms: Independently design and maintain internal React (TypeScript) interfaces and Python backend services that automate data ingestion and discovery, reducing lead times for application teams from weeks to minutes.
• Develop Data APIs: Build and maintain production-grade REST and gRPC APIs that serve as the high-performance interface between our Snowflake data layer and downstream consumer touchpoints.
• Modernize Data Operations: Implement a GitOps model for data using GitHub Actions and Argo/Kargo, integrating standardized logging, alerting, and automated observability into the heart of all data products.
• Innovate with AI: Leverage Cursor AI, MCPs, and other AI tooling to accelerate the data engineering SDLC, from optimizing complex SQL queries to automating schema migrations.
• Collaborate and Lead: Communicate with business leaders to translate complex data requirements into functional specifications while mentoring other engineers in modern data architecture and software best practices.
• Data Architecture: Design and optimize high-volume ETL/ELT pipelines using SQL, DBT, and Snowflake, ensuring data is modeled for both analytical and operational use cases.
• Internal Tooling (Full Stack): Develop and maintain internal-facing web applications using React that allow data owners to interact with, monitor, and configure their data pipelines.
• API Development: Architect and implement REST and gRPC APIs in Python that serve as the interface between our Snowflake data layer and downstream consumer applications.
• CI/CD & GitOps: Own the deployment lifecycle of data services and tools using GitHub Actions for CI and Argo/Kargo for continuous delivery and lifecycle management.
• Self-Service Platforms: Build "Data-as-a-Service" features, such as automated UI-driven ingestion workflows, reducing the reliance on manual data engineering tickets.
• AI Integration: Utilize modern AI development tools (e.g., Claude AI) to accelerate the development of both data pipelines and management interfaces.

Staff Data Engineer

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

• 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

• Build Pipelines: Design and maintain scalable data pipelines to ingest and enrich healthcare data using Databricks and Spark.
• Optimize Data Workflows: Improve data intake processes and optimize SparkSQL/Python workloads for performance, scalability, and cost efficiency.
• Design Data Models: Partner with senior engineers to develop data marts and semantic models that power analytics products and reporting.
• Ensure Quality: Monitor pipeline health, troubleshoot failures, and implement data validation and quality controls.
• Learn & Grow: Expand your knowledge of the full data lifecycle, cloud infrastructure (Azure/AWS), and healthcare data standards.

Data Engineer – Entry Level

• 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

• Meet with clients to understand their current business processes and needs to provide consulting services and direction on how to build or grow their current data strategy.
• Work with HBS Sales Solutions consultants to identify and grow opportunities within HBS client environments.
• Support and administer the underlining infrastructure and layout of a client data environment.
• Develop and design the process for the customer data collection process.
• Develop policies and procedures for the collection and analysis of data.
• Review customer sources to ensure integrity of the data collection process.
• Collaborate with the BI Data Developers to ensure the requirements are being met to build the right solution needed.
• Estimate development effort required to deliver data customer needs and requests.
• Use business analysis skillset to identify development needs for the purpose of streamlining and improving the operations of the organization for efficiency and profitability.
• Ability to work independently or as a team on project-based solutions for clients.
• Work with team mates to continue to grow and mature data services and delivery options for HBS clients.
• Based on experience, one may mentor other engineers in developing scalable, secure, high-performance BI and Data solutions.
• Meet annual billable hour goal, as defined by HBS. This number may change over time as the business evolves.

• 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

• Contribute to the development of data pipelines, APIs, and services supporting AI and analytics platforms
• Assist in building and maintaining cloud-based data workflows for structured and unstructured data
• Write clean, well-documented, and testable code following established standards
• Support AI platforms such as: Synthetic data generation components (Deep Synthetic Foundry)
• Implement clearly defined features or enhancements under guidance from senior engineers
• Work with SQL and cloud data platforms (e.g., Snowflake) to ingest, transform, and validate data
• Ensure data quality, reliability, and performance in production pipelines
• Partner with Data Scientists, Engineers, and Product team members on shared goals

Junior Developer – Data Engineer, AI Platforms

• Apply knowledge of health care and database management to analyze clinical data, and to identify and report trends.
• Design and build modern data-centric software applications to support clinical and operational processes across all parts of the healthcare system.
• Build data pipelines and transformations, data enrichment processes, provisioning layers, and user interfaces to meet the requirements of key initiatives.
• Mentor and assist less experienced Data Engineers.
• Participate in research and development of new technologies to help set up repeatable processes or templates for other Data Engineers to follow.
• Collaborate with Product, Platform, and Architecture teams to deliver on joint efforts.

• Management of Cogito suite of reporting tools
• Cogito Security management
• Maintenance of Cogito Related System Wide Settings
• Coordinates Analytics Processes/build across all Epic Applications
• Oversite of Cogito Tools build and provisioning standards
• Coordinating, auditing, troubleshooting technical system functionality – including reporting queues
• Represent Cogito in Epic governance groups
• Coordination & Management of Cogito related projects – including Epic Releases

Data Engineer II

• 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.

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