• Review customer data (CSVs, spreadsheets, software databases) to assess structure, quality, and completeness • Map legacy data fields to our platform’s schema using internal tools, AI tools (such as ChatGPT), or custom scripts • Transform, clean, and normalize customer data for migration into the platform • Execute and validate data imports in customer environments to ensure accuracy and completeness • Identify and troubleshoot errors throughout the migration process • Manually enter data where automation is not feasible • Become a product expert capable of setting up configurations on behalf of customers • Coordinate with onboarding specialists and customers to collect requirements and resolve data issues • Communicate migration status, clarify data concerns, and ensure customers understand field-level discrepancies • Collaborate with product and engineering teams to report bugs and suggest tooling improvements • Document migration processes and maintain internal guides to ensure consistency and scalability

• Engage in planning, analysis, design, and development of data management, data integration, data warehouse, and business intelligence solutions. • Engage in discussions with the team, clients, and project sponsors to determine project goals and set priorities. • Convert business and technical requirements into detailed data analysis and system design specifications. • Utilize database, programming, and ETL tools to implement and maintain data management and warehousing solutions, creating valuable data analytics assets for clients. • Work closely with clients and the BerryDunn team to analyze systems, gather requirements, map data pipelines, and assess data quality. • Conduct data quality analysis, investigate and resolve issues to ensure data integrity and accuracy. • Create and deliver reports and analytic applications using suitable business intelligence or custom tools to meet client needs. • Improve efficiency and reliability of client and BerryDunn data processes through innovative solutions and best practices. • Use Artificial Intelligence tools to enhance productivity and improve project deliverables. • Collaborate with other groups within BerryDunn to provide support for data and system analysis, data pipeline engineering, and data architecture. • Provide guidance and mentorship to junior team members, fostering a culture of continuous learning and improvement. • Keep abreast of the latest trends and technologies in data engineering, healthcare analytics, and data science to ensure BerryDunn remains at the forefront of the industry. • Ensure all data solutions comply with industry standards, regulations, and security protocols, particularly those specific to healthcare.

• Architect end-to-end data platforms on Azure, ensuring scalability, resilience, and future-proof design • Lead design reviews and architectural decisions for large or complex client data projects • Contribute to defining data engineering best practices, coding standards, and reusable frameworks across the team • Mentor and coach junior engineers on advanced Azure services, performance tuning, and cloud-native patterns • Collaborate with Data Architects to define data models, schemas, and integration patterns • Drive cloud cost optimization strategies, ensuring clients use the right services at the right scale • Evaluate and recommend new Azure tools and technologies for inclusion in Emergent Software’s offerings • Lead client advisory on data platform modernization, migration strategies (e.g., on-prem to Azure), and data engineering maturity • Contribute to thought leadership, such as blog posts, webinars, or speaking engagements on Azure data topics • Design, develop, and maintain data pipelines using Azure Data Factory, Synapse Pipelines, or Azure Batch • Build and optimize data storage layers (Azure OneLake, Azure SQL, Synapse Analytics) for structured, semi-structured, and unstructured data • Implement data transformation logic (ETL/ELT) using Spark, Python, SQL, or Azure Data Factory • Ensure data quality, validation, and lineage using tools like Azure Purview and integrate with governance frameworks • Collaborate with BI teams to prepare curated data models and tables for reporting in Power BI or downstream systems • Manage data security, privacy, and compliance (RBAC, encryption, sensitive data masking) across Azure services • Monitor and optimize pipeline performance and cost efficiency using Azure Monitor and cost management tools • Develop and maintain CI/CD pipelines for data projects using Azure DevOps • Document data engineering solutions and adhere to Emergent Software’s delivery standards and Agile practices

• Design and evolve a scalable, secure, cloud-native lakehouse platform leveraging Databricks, Microsoft Fabric (OneLake, Lakehouse, Data Factory), and dbt. • Define modeling patterns, governance frameworks, and engineering best practices across the data lifecycle. • Lead design reviews and guide teams in adopting scalable architectural patterns. • Drive long-term platform strategy and evaluate emerging technologies. • Design and implement batch and streaming data pipelines for healthcare data sources (EHR, claims, HL7/FHIR, APIs, flat files, databases). • Develop modular ingestion, quality, lineage, metadata, and observability frameworks that scale across domains. • Produce clean, analytics-ready datasets and data models for BI, analytics, and machine learning workloads. • Implement HIPAA-aligned access patterns and secure handling of PHI. • Architect Databricks workloads (clusters, jobs, Unity Catalog, Delta Lake) for reliability, performance, and cost efficiency. • Integrate Databricks and Microsoft Fabric with Azure services and enterprise systems. • Partner with product managers, data scientists, analysts, clinicians, and business stakeholders to translate healthcare data needs into scalable solutions. • Lead cross-functional initiatives that modernize and unify the organization’s data ecosystem. • Mentor senior and mid-level engineers; elevate team capability through technical coaching and standards. • Drive roadmap planning, platform evolution, and long-term data strategy. • Champion engineering excellence, reliability practices, documentation quality, and governance.