• Design, develop, and maintain scalable, secure, and reliable data pipelines using Databricks, Spark, and cloud-native technologies. • Build and optimize metadata-driven ETL/ELT pipelines. • Integrate data from multiple internal and external systems into analytics-ready datasets. • Support AI/ML and advanced analytics initiatives through high-quality data assets. • Implement data quality controls, monitoring, and validation frameworks. • Troubleshoot data pipeline failures and resolve production issues. • Optimize data processing performance and ensure operational reliability. • Collaborate with Data Science, Analytics, Product, and Engineering teams. • Develop curated datasets for reporting and visualization platforms such as Tableau and Power BI. • Provide technical mentorship and contribute to engineering best practices.

• 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.

• Seeking an experienced Technical Architect to design scalable, high-performance data solutions. • Translate business and functional requirements into technical architecture and designs. • String hands-on knowledge on Python, Teradata, Redshift. • Must have done a couple of Data Platform modernization programs on AWS. • Strong knowledge on AWS PostgreSQL, Redshift and Teradata. • Architect data solutions using AWS, PostgreSQL. • Design and review high-quality Python solutions. • Lead SQL design and performance tuning on large datasets. • Provide technical leadership, code reviews, and best-practice guidance. • Collaborate with cross-functional teams for end-to-end delivery.

• Design, build, and maintain robust ETL/ELT data pipelines on Google Cloud Platform • Write efficient, production-grade SQL for data transformation, modeling, and analysis • Develop and maintain Python -based data processing scripts and automation • Build and manage workflow orchestration using Apache Airflow (e.g. Cloud Composer) • Work with Datastream and other GCP services to ingest and replicate data from source systems • Support and maintain data models and dashboards in Looker • Implement data quality checks, monitoring, and alerting for pipelines • Troubleshoot and resolve pipeline and data issues • Collaborate with analysts, data scientists, and business stakeholders to understand data needs • Document pipelines, data flows, and processes