• Lead and contribute to end-to-end Databricks implementations for clients, including data migration, Lakehouse architecture, and pipeline development • Gather technical requirements, design solutions, and present recommendations to client stakeholders (technical and business) • Build scalable ETL/ELT pipelines using PySpark, Delta Lake, Delta Live Tables (DLT), and Databricks Workflows • Design and implement Databricks Genie • Design and implement semantic layers • Use Databricks AI features to accelerate development, debugging, and code optimization • Design and implement Lakebase architectures for operational and analytical workloads, including transactional data use cases • Develop solutions using SDLC best practices, including modular code design, testing, and documentation • Use Git based version control with proper branching strategies • Implement CI/CD pipelines for Databricks asset • Implement data quality checks, validations, and expectations within workflows • Design and implement Unity Catalog governance, security, and lineage solutions • Optimize Databricks workloads for performance, cost, and reliability (Photon, cluster policies, Liquid Clustering, Auto Loader, etc.) • Integrate Databricks with client ecosystems (Azure, AWS, GCP, Snowflake, Kafka, legacy systems, etc.) • Support client workshops, proof-of-concepts (POCs), and knowledge transfer sessions • Deliver projects following consulting methodologies while meeting quality, timeline, and budget expectations • Document architectures, runbooks, and best practices for client use • Participate in solutioning activities (scoping, estimation, technical demos) as needed

• Guide clients on optimizing their data environment to work most effectively and efficiently for them. Clarify management objectives through data solutions. • Develop system engineering, integrations, and architectures based on client needs. • Implement and provide advice on data warehouse solutions, ETL pipelines, and business intelligence reporting tools. • Develop a data model around stated use cases to capture client’s KPIs and data transformations. Validation and testing of data models. • Teach technical data modeling concepts to a variety of audiences, including developers, data architects, business users, and IT professionals. • Support, maintain, and document clients’ data environments. • Work within a project management framework to meet objectives, understand scope, and impact of your work across an organization. • Work with the Sales and Marketing teams to develop Thought Leadership and support our sellers by talking to clients about DAS42’s capabilities.

• 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