Title: Information Technology Data Platform Engineer II Location: Remote, US Job Description: Salary Range:$130,000.00 To $145,000.00 Annually Remote, Full Time Reports to: Head of Data/IT Current or future sponsorship is not available for this role About the Role We are looking for a hands-on Data Platform Engineer to own, operate, and evolve our modern cloud data stack. You will be the primary technical owner of our data infrastructure — responsible for keeping data flowing reliably from source systems into Snowflake and ensuring clean, trusted data reaches our business teams through Power BI. This is a high-impact role on a small, focused team where your work will be directly visible to the business. What You Will Work On DATA INGESTION - Own and manage Fivetran connectors across all source systems including NetSuite, HubSpot, ADP, SQL Server, SAP HANA, and SharePoint - Configure and monitor sync schedules, column exclusions, and incremental load strategies to control cost and reliability - Troubleshoot connector failures and proactively manage schema drift from upstream sources DATA TRANSFORMATION - Maintain and extend our dbt project across three layers: staging (L1), core dimensions and facts (L2), and business-ready marts (L3) - Write and optimize SQL models using incremental merge strategies and watermark patterns - Author and maintain dbt tests, model documentation, and source freshness checks to ensure data quality - Support the buildout of our finance EDW including GL activity, planning data from Workday Adaptive, and NetSuite financials ORCHESTRATION & PIPELINE OPERATIONS - Manage end-to-end pipeline scheduling and monitoring, ensuring daily refreshes complete reliably before business hours - Maintain the integration between Fivetran, dbt, and Power BI dataset refresh triggers - Build and maintain alerting so pipeline failures are caught and communicated before the business is impacted DATA WAREHOUSE & GOVERNANCE - Manage Snowflake environments including databases, schemas, roles, warehouses, and cost controls - Implement and maintain access controls and role-based permissions across the data platform - Contribute to data catalog and lineage documentation to support a growing team and reduce knowledge concentration risk COLLABORATION - Partner with Finance, Sales, and Operations teams to understand reporting requirements and translate them into reliable data models - Support and mentor the junior member of the data team as they develop their skills - Work closely with the incoming NetSuite implementation team to ensure clean data integration into the warehouse What We Are Looking For REQUIRED - 3 to 5 years of experience in data engineering, analytics engineering, or a closely related role - Hands-on experience with dbt (Core or Cloud) including incremental models, tests, macros, and documentation - Proficiency with Snowflake including schema design, query optimization, warehouses, and role-based access control - Experience with a managed ingestion tool such as Fivetran and dlt including connector configuration and monitoring - Strong SQL skills with the ability to write and debug complex analytical queries - Familiarity with ELT pipeline patterns and medallion-style data warehouse architecture - Experience troubleshooting pipeline failures independently and communicating issues clearly to non-technical stakeholders - Comfort working autonomously in a small team environment with limited oversight PREFERRED - Experience connecting to on-premises source systems (SQL Server, SAP HANA, Oracle) via ODBC or CDC tooling - Familiarity with ERP financial data in NetSuite, SAP, or similar, particularly GL structures and chart of accounts - Exposure to Power BI including dataset refresh management and understanding of how semantic models consume warehouse data - Experience with Git-based workflows and basic CI/CD practices for data projects - Prior involvement in an EDW build or dimensional modeling project (star schema, slowly changing dimensions) What Success Looks Like In your first 30 days: - You have completed a full walkthrough of the existing stack with our departing team member - You can independently run, monitor, and troubleshoot the daily pipeline end to end - You have documented any gaps or risks you have identified in the current setup In your first 90 days: - All Fivetran connectors are live and the dlt migration is complete - dbt is running in dbt Cloud or Snowflake Workspaces with jobs, alerting, and documentation in place - The Finance team has improved confidence in the GL data flowing through the warehouse In your first year: - The data platform is running reliably with minimal intervention and strong business trust - The NetSuite data integration is live and the finance EDW is serving reporting needs - You have grown the junior team member's capability and reduced single-person dependency on yourself Our Stack You will be working with the following technologies from day one: - Ingestion: Fivetran, dlt - Warehouse: Snowflake - Transformation: dbt (Cloud or Snowflake-native) - Reporting: Power BI - Sources: NetSuite, HubSpot, ADP, SQL Server, SAP HANA, Workday Adaptive, SharePoint, Oracle DB About the Team Our data team is small and our work has direct impact on how the business makes decisions. We are in a period of transition migrating from SAP to NetSuite, rebuilding trust in our data, and modernizing our platform. The person who takes this role will help shape what our data function looks like over the next few years. We are looking for someone who takes ownership, communicates clearly, and genuinely cares about the quality of the data products they ship. Company Summary: Why Join Us? RTI Surgical is now Evergen! This rebranding reflects our strategic evolution as a leading CDMO in regenerative medicine and comes at the end of a significant year for the business, including the successful acquisitions of Cook Biotech in IN. and Collagen Solutions, MN. Our new brand identity emphasizes our unique positioning as the only CDMO offering a comprehensive portfolio of allograft and xenograft biomaterials at scale. Evergen is a global industry-leading contract development and manufacturing organization (CDMO) in regenerative medicine. As the only regenerative medicine company that offers a differentiated portfolio of allograft and xenograft biomaterials at scale, Evergen is headquartered in Alachua, FL, and has manufacturing facilities in West Lafayette, IN., Eden Prairie and Glencoe, MN., Neunkirchen, DE., Glasgow, UK., and Marton, NZ. Evergen is a global industry-leading contract development and manufacturing organization (CDMO) in regenerative medicine. As the only regenerative medicine company that offers a differentiated portfolio of allograft and xenograft biomaterials at scale, Evergen provides customers across a diverse set of market segments with leading-edge expertise, scale, and flexibility across end-to-end services including design, development, regulatory support, verification and validation, manufacturing, and supply chain management. Evergen is headquartered in Alachua, FL, and has manufacturing facilities in West Lafayette, IN., Eden Prairie and Glencoe, MN., and Neunkirchen, Germany. Evergen is rooted in a steadfast commitment to quality, integrity, and patient safety with a focus on five key values: • Accountable: We own our actions and decisions. • Agile: We embrace change to stay ahead of the curve and evolve to drive innovation and growth. • Growth Mindset: We embrace challenges as opportunities for continuous learning. • Customer-Centric: We prioritize customers at every touch point. • Inclusive: We thrive on the richness of our diversity and ensure every voice is heard, respected, and celebrated. At Evergen, we are committed to fostering an inclusive workplace where we embrace the richness of our diversity and ensure that every voice is heard, respected, and celebrated. We believe that by embracing diversity and promoting inclusivity, we not only uphold our values but also strengthen our position as the CDMO of Choice in regenerative medicine solutions. We recognize that cultivating a growth mindset is essential to our success, and we are dedicated to continuous learning and improvement in our diversity, equity, and inclusion efforts. Through accountability and action, we strive to create an environment where individuals can thrive, innovate, and contribute their unique perspectives to drive our collective success. Montagu Private Equity (“Montagu”), a leading European private equity firm, acquired Evergen in 2020 and has supported the transformation of the company to its next level of potential. #LI-Remote

• Scope the work and see it through • Lead architecture during discovery • Stay on as the design owner during build • Architect AI-ready data foundations • Design end-to-end cloud platforms for retail clients • Build engineering rigor in by default – automated pipelines, infrastructure-as-code, CI/CD, testing, and observability • Co-sell with Business Developers, Key Account Managers, and Strategists

• Lead architecture during discovery – translating each client's business model, KPIs, and AI goals into a target-state design and implementation plan. • Stay on as the design owner during build, reviewing pipelines, models, and BI work from data and analytics engineers, and unblocking the team when they hit hard calls. • Architect AI-ready data foundations – the semantic layers, feature stores, vector databases, and embeddings pipelines that ML models, GenAI apps, and AI agents need to work reliably. • Design end-to-end cloud platforms for retail clients: ingestion, storage, transformation, modeling (dimensional, Data Vault, One Big Table, or whatever fits), BI, and CDP. • Build engineering rigor in by default – automated pipelines, infrastructure-as-code, CI/CD, testing, and observability – so each platform gets cheaper and more reliable over time, not the opposite. • Co-sell with Business Developers, Key Account Managers, and Strategists – shaping proposals, defending estimates, and presenting architectures to client stakeholders who range from skeptical CTOs to non-technical executives.

• Lead architecture during discovery – translating each client's business model, KPIs, and AI goals into a target-state design and implementation plan. • Stay on as the design owner during build, reviewing pipelines, models, and BI work from data and analytics engineers, and unblocking the team when they hit hard calls. • Architect AI-ready data foundations – the semantic layers, feature stores, vector databases, and embeddings pipelines that ML models, GenAI apps, and AI agents need to work reliably. • Design end-to-end cloud platforms for retail clients: ingestion, storage, transformation, modeling (dimensional, Data Vault, One Big Table, or whatever fits), BI, and CDP. • Build engineering rigor in by default – automated pipelines, infrastructure-as-code, CI/CD, testing, and observability – so each platform gets cheaper and more reliable over time, not the opposite. • Co-sell with Business Developers, Key Account Managers, and Strategists – shaping proposals, defending estimates, and presenting architectures to client stakeholders who range from skeptical CTOs to non-technical executives.