• Contribute to the design and development of scalable data pipelines on AWS (Glue, S3, Athena, Step Functions). • Build and optimize batch and streaming ingestion pipelines, including CDC-based architectures. • Support the design and implementation of data models aligned with business and analytics needs. • Ensure data quality, reliability, and performance across pipelines and datasets. • Collaborate with cross-functional teams to align technical solutions with business requirements. • Apply best practices in data engineering, including modular design, reusability, and governance. • Leverage AI-assisted development tools to improve productivity and code quality. • Proactively identify risks, bottlenecks, and optimization opportunities across data workflows.

• Design, build, and optimize scalable data pipelines supporting enterprise healthcare analytics and reporting use cases • Lead automation efforts across ingestion, transformation, orchestration, and operational monitoring workflows • Develop and maintain ingestion frameworks for structured and semi-structured healthcare data sources • Build and expand semantic data layers within GCP and BigQuery to support consistent, trusted business reporting and analytics consumption • Design scalable ELT/ETL workflows leveraging modern cloud-native architecture patterns • Partner closely with architects, analytics teams, and business stakeholders to translate data requirements into scalable technical solutions • Optimize BigQuery performance, partitioning, and cost management strategies • Implement engineering best practices for testing, observability, reliability, and deployment automation • Support data governance, data quality, and metadata management initiatives across the platform • Mentor junior engineers and provide technical leadership across delivery teams

• Design and own the ingestion architecture that moves data from 20+ enterprise source systems into the AWS Marketing Data Layer • Define scalable ingestion patterns for both legacy and direct source integrations across 18 business and data domains • Architect and document data contracts between source systems and the data layer, including schema standards and versioning, SLAs and freshness expectations, data quality and governance requirements • Design and optimize AWS S3 data lake structures, including raw, staged, and curated zones, partitioning and storage strategies, Lake Formation access controls and governance policies • Define Change Data Capture (CDC) and streaming ingestion patterns to support near-real-time data freshness requirements • Translate conceptual and logical architecture into detailed, implementable technical specifications for distributed and nearshore Data Engineering teams • Partner with engineering leadership and client stakeholders to ensure scalability, performance, maintainability, and operational excellence across the platform • Provide technical leadership and architectural guidance throughout the delivery lifecycle

• Design, develop, and optimize scalable data pipelines supporting enterprise healthcare analytics and reporting initiatives • Build and automate ingestion frameworks for structured and semi-structured healthcare data sources • Develop and expand semantic data layers in GCP and BigQuery to support trusted, consistent analytics consumption across business teams • Design and implement scalable ELT/ETL workflows using modern cloud-native architecture patterns • Automate orchestration, monitoring, and operational processes across the data platform • Partner with architects, analytics teams, and business stakeholders to translate requirements into scalable technical solutions • Optimize BigQuery performance, partitioning, and query efficiency to improve scalability and cost management • Implement engineering best practices for testing, observability, reliability, deployment automation, and operational support • Contribute to data governance, metadata management, and data quality initiatives across the platform • Mentor junior engineers and contribute to a collaborative, high-performing engineering culture