• Design, build, and maintain scalable data pipelines, integrations, and AI workflows • Develop reliable and maintainable ETL/ELT systems that support analytics, operational reporting, and AI-driven products • Contribute to the architecture and evolution of the company’s data platform and AI infrastructure • Continuously optimize data architecture to support evolving business and product requirements • Develop infrastructure automation and deployment workflows to improve engineering velocity and operational consistency • Implement infrastructure as code (IaC) practices using tools such as Terraform or CloudFormation • Build and maintain CI/CD pipelines and automated testing workflows • Develop monitoring, alerting, and observability solutions for data and AI systems • Improve reliability, scalability, and operational efficiency through automation • Contribute to production-ready AI systems and workflows where they provide measurable business value • Mentor junior and mid-level engineers through code reviews, pairing, and technical guidance

• Work on the development and evolution of the new corporate data platform; • Build and optimize scalable, distributed data pipelines; • Develop solutions using Databricks and Apache Spark; • Work on the integration, transformation and provisioning of data to various business areas; • Support DataOps initiatives, automation, and platform observability; • Collaborate with multidisciplinary teams in an agile environment; • Ensure engineering best practices, quality, versioning, and CI/CD; • Contribute to the architecture and continuous improvement of the cloud platform.

• Own a Data, AI/ML product portfolio spanning Personalization and Marketing • Define and drive the technical roadmap, balancing innovation, and agile delivery with operational stability and reliability • Lead the strategic shift from rule-based campaigns to AI and agent-based orchestration • Oversee full-scale AI/ML & Data pipelines including deep-learning models and optimization capabilities • Lead architecture reviews, design forums, and technical sessions across Data Science, Engineering, and Technology teams • Ensure engineering standards are maintained across the portfolio - covering solution design, security, and Data, AI Ops practices • Drive agile delivery within the Squad model, managing dependencies across a matrixed environment, and removing blockers • Coach and grow engineering leaders and senior ICs and build a strong internal talent pipeline • Serve as a trusted partner to Senior stakeholders across Data Science and Business teams • Translate complex technical concepts into clear, business-relevant narratives for Executive and non-technical audiences

• Design, build, and optimize robust ETL/ELT processing pipelines to ingest and transform complex healthcare data from disparate sources • Architect and maintain scalable data solutions utilizing Snowflake as our primary data cloud, leveraging both Azure and AWS environments • Build and maintain a robust semantic layer to translate complex data structures into business-friendly metrics • Collaborate closely with data leadership to synchronize disparate data streams, ensuring data quality and the successful deployment of Customer 360 initiatives • Ensure all data architecture, storage, and processing strictly adhere to healthcare compliance standards (e.g., HIPAA, HITRUST) and best practices for handling Protected Health Information (PHI)