• Design and implement data ingestion pipelines using CDC/CT (Change Tracking) from SQL Server sources • Build and maintain medallion architecture (Bronze → Silver → Gold) data layers in Snowflake • Develop data transformation models using tools like dbt, AWS Glue, or Snowflake native features (stored procedures, tasks) • Implement SCD Type 1 and Type 2 patterns for historical data tracking • Design and implement Row-Level Security (RLS) for multi-tenant data access • Create and optimize Snowflake objects including tables, views, streams, tasks, and stored procedures • Design data models supporting complex reporting requirements for absence management, workforce analytics, and substitute tracking • Ensure Gold layer is optimized for BI tool consumption (Power BI, Tableau, or other reporting tools) • Establish data quality checks, monitoring, and alerting mechanisms • Document technical designs, data dictionaries, and lineage documentation • Mentor junior team members and conduct code reviews

• Analyze and interpret complex schemas across multiple source system databases to support migration and integration requirements. • Develop conceptual, logical, and physical data models aligned with business rules, performance expectations, and regulatory constraints. • Define entity relationships, primary/foreign key structures, indexing strategies, and normalization/denormalization approaches. • Collaborate with product SMEs to map configuration and transactional datasets for a structured two‑phase migration process. • Establish robust validation frameworks covering schema compatibility, data type integrity, truncation detection, and referential consistency. • Design and build scalable, reliable data pipelines to support warehousing, analytics, and migration workflows. • Apply core data engineering principles—data modeling, partitioning, indexing, and performance tuning—independent of specific tools or technologies. • Troubleshoot data and pipeline issues efficiently and operate independently with minimal supervision. • Leverage cloud platforms (AWS, Azure, or GCP) for data services, storage, and compute infrastructure as needed.

• Conduct a comprehensive current-state assessment of our UDMH and data integration landscape. • Document institutional knowledge and undocumented architecture currently held by a single contributor, with the goal of eliminating personnel-based single points of failure (SPOFs). • Develop and maintain architecture artifacts including data flow diagrams, system dependencies, integration points, and technical documentation. • Lead technology evaluations and proof-of-concept efforts for emerging interoperability tools (e.g., FHIR servers, interface engines, AI/ML-driven transformation platforms) to validate scalable and innovative architecture components. • Evaluate proprietary vs. non-proprietary paths for UDMH, providing technical and strategic recommendations based on scalability, cost, flexibility, and long-term sustainability. • Define and communicate a future-state data architecture roadmap that supports scalability, fault tolerance, and evolving organizational needs. • Design and implement interoperable data solutions using standards such as FHIR and HL7 to support seamless integration across healthcare systems. • Provide subject matter expertise in source-to-target mapping across data systems and specifications • Oversee the integration of diverse data sources into a unified, governed data platform to support both clinical and operational use cases. • Define data models, classifications, and types that align with business and clinical application needs. • Establish KPIs and monitoring frameworks to assess system performance, availability, data quality, and architectural resilience over time. • Lead and enforce data governance frameworks to ensure compliance with HIPAA and other regulatory standards, emphasizing data quality, privacy, and security. • Continuously optimize and automate data integration workflows, improving availability, performance, and operational efficiency. • Collaborate with clinical, technical, and leadership stakeholders to align data strategies with business objectives and patient outcomes. • Partner with security and IT teams to ensure strong authentication, encryption, and access controls for sensitive healthcare data. • Serve as a mentor and knowledge-sharing leader for data engineers, analysts, and developers, ensuring institutional knowledge is distributed and scalable.

• Take ownership of Planful post-implementation, ensuring the system is fully functional, stable, and aligned with business needs. • Work with FP&A to optimise Planful models, dimensions, and reporting templates, improving usability and adoption across Finance. • Design, build, and maintain ETL/ELT pipelines feeding the central data warehouse. • Integrate data from ERP (Xero/NetSuite/Intacct), CRM (Salesforce/HubSpot), billing, FP&A tools (Planful) and banking platforms. • Develop and maintain Planful reports and dashboards used for monthly performance reviews and Board reporting. • Automate repetitive Finance processes: reconciliations, journal postings, ARR roll-forward checks, variance reporting. • Use APIs, Python scripts, and AI agents to reduce manual work across AP, AR, and FP&A. • Partner with Finance and FP&A to improve close speed and forecast accuracy through automation. • Ensure SaaS metrics (ARR, NRR, CAC payback, churn) are automatically reconciled between source systems and dashboards. • Troubleshoot Planful data or workflow issues, liaising with internal teams and Planful support as needed. • Develop scripts/AI workflows for automated management reports and Board packs.