• Build Pipelines: Design and maintain scalable data pipelines to ingest and enrich healthcare data using Databricks and Spark. • Optimize Data Workflows: Improve data intake processes and optimize SparkSQL/Python workloads for performance, scalability, and cost efficiency. • Design Data Models: Partner with senior engineers to develop data marts and semantic models that power analytics products and reporting. • Ensure Quality: Monitor pipeline health, troubleshoot failures, and implement data validation and quality controls. • Learn & Grow: Expand your knowledge of the full data lifecycle, cloud infrastructure (Azure/AWS), and healthcare data standards.

• Design, build, and maintain scalable data engineering frameworks and platform utilities used across engineering teams • Develop reusable patterns, templates, and abstractions to standardise and accelerate delivery • Define and evolve platform architecture decisions, ensuring scalability, maintainability and consistency • Design and implement CI/CD pipelines and automation frameworks to improve engineering velocity • Define and enforce engineering standards for testing, code quality, deployment and documentation • Identify and eliminate manual or repetitive processes through automation and tooling improvements • Integrate AI-assisted development tools into engineering workflows to improve productivity • Develop and maintain AI engineering assets such as coding guidelines, prompt frameworks and reusable agent configurations • Lead the development and operational support of core data transformation frameworks (including dbt Core at enterprise scale) • Investigate and resolve framework-level issues, including deployment failures, dependency conflicts and production incidents • Support onboarding and enablement of engineering teams adopting platform tooling • Act as the main technical point of contact for platform and framework-related queries • Partner with engineering teams to identify pain points and translate them into platform improvements • Ensure platform tooling meets security, compliance and operational requirements • Conduct and support code and design reviews across platform components • Monitor platform health, performance and adoption, iterating based on feedback and metrics • Contribute to documentation, developer guides and enablement materials to improve usability and adoption

• Designs and builds modern data-centric software applications to support clinical and operational processes • Builds data pipelines and transformations, data enrichment processes, provisioning layers, and user interfaces • Works closely with the Product, Platform, and Architecture teams to deliver on joint efforts

• Operate as the data engineering expert on the Internal Efficiencies team, collaborating closely with other engineers, data scientists, solution / product experts, and customer success leaders to deliver against user / customer deliverables • Contribute to defining data engineering deliverables from inception to completion, ensuring timely delivery and high-quality outputs • Leverage agentic AI tools on a daily basis to deliver code and improve your productivity • Develop and maintain strong relationships with all Internal Efficiencies team members as well as engineering subject-matter experts across Viaduct • Work with customer success and solution / product team members to scope user/customer needs and translate them into actionable analytics solutions • Deliver against deliverables with quality and speed, contributing new features to solutions and products, refactoring code, improving tests, automating manual processes, and owning other engineering tasks • Stay updated with the latest trends and related technologies and apply them to improve our software