• Lead the architecture and evolution of scalable, distributed data pipelines, ensuring high availability and performance at scale • Design and implement robust data models to support reporting and advanced data applications • Build and maintain distributed web scraping systems using tools such as Playwright, Selenium, and BeautifulSoup • Develop systems capable of handling anti-scraping measures, proxy rotation, and high-volume data extraction • Integrate AI and LLMs into engineering workflows for code generation, automation, and optimization • Apply prompt engineering techniques to improve data processing, documentation, and troubleshooting • Identify and implement system and process improvements to optimize performance and efficiency • Manage and scale cloud-based data infrastructure, including data warehouses, object storage, and search systems • Deploy and maintain containerized workloads using Kubernetes • Implement data quality monitoring and governance processes to ensure accuracy and reliability • Mentor junior engineers through code reviews, documentation, and knowledge sharing • Communicate technical concepts clearly and provide business context for engineering decisions

Job Description Summary Join us in building clinical grade data platforms that power meaningful insights across connected devices, diagnostics, and digital health. In this role, you will shape scalable data architecture on AWS that enables secure ingestion, governance, analytics, and responsible AI/ML. Your work will have a direct impact on improving patient outcomes and supporting clinicians, product teams, and partners around the world. We welcome applicants from all backgrounds—especially those historically underrepresented in tech—and encourage candidates who meet most, but not all, of the qualifications to apply. We value curiosity, collaboration, and a growth mindset. Job Description What You’ll Do Data Platform Architecture - Design and evolve cloud‑native data platforms using S3, Lake Formation, Glue (catalog/ETL), Athena, EMR/EKS‑Spark, Redshift (including serverless), and Kinesis/MSK for streaming. - Define lake and lakehouse patterns, real‑time and batch pipelines, and governed self‑service analytics capabilities. Governance & Privacy - Implement PHI tokenization/pseudonymization, fine‑grained access controls (column/row level), Macie discovery, encrypted storage (KMS), and data retention/lineage strategies using Glue and tags. - Apply DLP and other privacy‑preserving controls aligned with HIPAA, GDPR, HITRUST, and FDA/ISO frameworks. Interoperability - Enable data exchange using FHIR, DICOM, HL7, and device telemetry through IoT Core into streaming and lake layers. ML & MLOps - Build governed ML workflows with SageMaker pipelines, model registry, lineage tracking, explainability, and bias reporting. - Support dataset versioning and incorporate human‑in‑the‑loop processes when needed. Self‑Service Data & Data Products - Lead data mesh/product governance, enable Redshift/Athena consumption, support DataZone cataloging and access workflows, and utilize Clean Rooms for privacy‑preserving collaboration. Reliability & Performance - Architect for resiliency across multi‑AZ/multi‑region deployments, including S3 replication, lifecycle management, partitioning/compaction, and cost‑efficient performance tuning. Validation & Auditability - Maintain validation packages for regulated analytics and AI pipelines, including traceable lineage and CFR Part 11 evidence. Required Qualifications This role will need to work out of the central time zone. - 12+ years of experience in data or analytics platforms. - 6+ years leading AWS data architecture at scale. - Deep expertise with S3, Lake Formation, Glue, Athena, EMR, Redshift, Kinesis/MSK, and SageMaker. - Experience governing PHI and regulated ML workflows. Preferred Qualifications - Experience with table formats such as Apache Iceberg, Delta Lake, or Hudi, and ACID‑on‑lake patterns. - Knowledge of CDC ingestion (DMS). - Familiarity with curated imaging pipelines (DICOM) and vector search for clinical text/notes. - FinOps practices for data platforms (tiering, compression, query optimization). What We Offer - A collaborative environment where diverse perspectives are valued. - Opportunities for ongoing learning, mentorship, and professional growth. - Flexibility, autonomy, and support from peers across engineering and product teams. - The chance to build solutions that have a real impact in healthcare. #LI-LRG1 #LI-Onsite #LI-Hybrid #LI-Remote We will not sponsor individuals for employment visas, now or in the future, for this job opening. For U.S. based positions only, the pay range for this position is $188,000.00-$282,000.00 Annual. It is not typical for an individual to be hired at or near the top of the pay range and compensation decisions are dependent on the facts and circumstances of each case. The specific compensation offered to a candidate may be influenced by a variety of factors including skills, qualifications, experience and location. In addition, this position may also be eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). GE HealthCare offers a competitive benefits package, including not but limited to medical, dental, vision, paid time off, a 401(k) plan with employee and company contribution opportunities, life, disability, and accident insurance, and tuition reimbursement. Additional Information GE HealthCare offers a great work environment, professional development, challenging careers, and competitive compensation. GE HealthCare is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE HealthCare will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). While GE HealthCare does not currently require U.S. employees to be vaccinated against COVID-19, some GE HealthCare customers have vaccination mandates that may apply to certain GE HealthCare employees. Relocation Assistance Provided: No Application Deadline: May 29, 2026

• Design, build, and maintain scalable, resilient data pipelines; • Work with large volumes of structured and unstructured data; • Ensure data quality, consistency, and data governance; • Collaborate with software engineers, analysts, and data scientists; • Participate in technical decisions about data architecture and tooling;

• The professional will work on building data pipelines, developing CDS Views and integrating SAP → Azure Data Factory → Azure SQL, ensuring data quality, consistency and availability for analytics and strategic dashboards. • The Senior Data Engineer will be responsible for developing and evolving data pipelines and integrations between the SAP environment and the corporate Data Warehouse in Azure, ensuring data quality, consistency and scalability. • Will work directly on extracting data via CDS Views, integrating with Azure Data Factory and building data layers in Azure SQL, supporting the definition of the final Data Warehouse architecture. • The professional will also help ensure the data environment is prepared to support strategic dashboards and the migration of the SICOF system to SAP. • Create and adjust CDS Views in SAP; • Extract data via Azure Data Factory; • Enrich the Data Warehouse (Azure SQL); • Ensure data quality and consistency; • Support the construction of the final data architecture. • Develop advanced CDS Views in SAP (associations, annotations, performance tuning); • Adjust and optimize existing views; • Integrate data SAP → Azure Data Factory → Azure SQL; • Implement ingestion pipelines and incremental loads; • Ensure data quality, consistency and integrity; • Support the definition of the Data Warehouse architecture; • Assess the need for dimensional modeling (Star Schema); • Support the evolution of data governance; • Collaborate with data, BI and architecture teams to ensure availability and reliability of information.