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Own end-to-end application and technical architecture for reporting, analytics, data integration, and transaction-based processing solutions across on-premises platforms and future cloud services.
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Translate business, operational, and regulatory requirements into maintainable system designs, including configurable business rules, effective-dated reference data, traceability, auditability, and reconciliation.
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Design and lead modernization of legacy ETL, SQL logic, Linux/Unix scripts, and manual processing into Python-based applications and automation, using enterprise data platforms efficiently for high-volume processing.
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Provide hands-on technical leadership across architecture, development, code review, testing, deployment, performance tuning, and production support to unblock the team.
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Build proof-of-concepts that compare solution options, document measurable results, and support architecture decisions with the customer and leadership.
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Define technical architecture across web/UI, API and service layers, processing engines, databases, orchestration, scheduling, security, identity/access, monitoring, and backup/recovery, in partnership with infrastructure, security, cloud, and data peers.
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Lead BI and analytics delivery including enterprise report design, drill-down and detail reporting, reconciliation, data quality, and modernization of legacy reporting capabilities.
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Establish engineering practices using Azure DevOps or equivalent – epics and user stories, repositories, branching, pull requests, code review, CI/CD, release traceability, and production deployment – and apply AI and automation with appropriate governance to accelerate analysis, conversion, documentation, and testing.
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Produce architecture diagrams, solution designs, technical specifications, dataflow and interface documentation, implementation of roadmaps, estimates, risks, and operational support models.
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Lead and mentor architects, data engineers, ETL and BI developers, modelers, analysts, and testers; build backup coverage and develop team capability.
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Present architecture, options, risks, and recommendations concisely to customer stakeholders and Optum leadership, creating clarity across ambiguous scope, legacy constraints, and accelerated timelines.
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Bachelor’s degree in computer science, engineering, information systems, or related field, or equivalent professional experience.
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10+ years of information technology experience, including senior-level application, solution, technical, or data architecture for large enterprise systems.
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Proven experience architecting enterprise data warehouse, reporting, analytics, and claims or transaction-processing solutions from the ground up – architecture, infrastructure, design, development, proof-of-concept, testing, implementation, and support.
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Hands-on development experience with Python and advanced SQL, including automation, data processing, troubleshooting, and code review.
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Experience designing application architecture including APIs, services, web components, databases, security, identity/access, job scheduling, monitoring, and environment management.
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Hands-on BI/reporting experience with BusinessObjects, Power BI, Tableau, SAS, or comparable enterprise reporting tools.
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Experience implementing source control, branching, pull requests, CI/CD, and automated deployment in Azure DevOps or a similar platform.
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People leadership experience leading BI, data, and engineering staff, with strong written, verbal, and presentation skills for customer and executive audiences.
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Healthcare payer, Medicaid, managed care, or health-insurance experience, including claims, adjustments, voids, eligibility, providers, payments, and regulatory reporting data.
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Federal reporting experience, especially CMS-64, quarterly expenditure reporting, reconciliation, certification, or related state Medicaid reporting.
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On-premises to cloud migration experience with Microsoft Azure, Azure Data Factory, Databricks, Data Lake Storage, Snowflake, or comparable cloud data services.
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ETL/ELT and data engineering experience with Informatica, Teradata, or comparable enterprise integration and large-scale MPP data warehouse platforms.
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Applying AI-assisted engineering or automation to code conversion, source-to-target mapping, testing, documentation, and data-quality activities within enterprise governance.
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Data modeling across conceptual, logical, and physical models, effective-dated reference data, dimensional models, and transaction lineage; experience with Linux/Unix scripting and job orchestration.
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Knowledge of HIPAA, PHI, PII, security architecture, and public-sector compliance expectations; advanced degree or relevant architecture, cloud, data, or agile certifications.
