Role Description The Lead Azure Data Engineer is responsible for migration, governance, and data integration for the implementation of new Microsoft Cloud based document routing and task management system. They will ensure alignment with the technical, security, and document management requirements such as: - Leading data discovery and mapping - Validating metadata standards - Managing data migration into Dataverse and SharePoint Online - Ensuring proper implementation of retention, auditability, and role-based access controls within the Microsoft SaaS architecture Qualifications - A minimum of 6 years IT experience with focus on data integration and data migration using Azure Data Services - Must have strong experience designing data models, configuring Dataverse tables, managing relationships, enforcing row- and field-level security, and supporting workflow-driven data structures - Knowledge of SharePoint Online document libraries, metadata schemas, retention labels, records management, and large-volume document storage design - Proficiency in data mapping, transformation, validation, and migration using APIs, Azure services, Power Automate, and REST-based integrations - Experience with Azure data services (e.g., Azure Data Factory, Azure SQL, Azure AI Search, Azure storage components) in FedRAMP-aligned environments - Understanding of producing audit logging, identity integration (Entra ID), and support for records retention and legal hold requirements - Ability to structure data for Power BI dashboards, performance reporting, and AI-enabled search and discovery - Establishing metadata standards, validation rules, lifecycle controls, and ensuring data accuracy and consistency across enterprise workflows - Bachelors degree or equivalent - Must be a US Citizen Requirements - Microsoft Azure data services certifications - Active Federal Government Public Trust clearance Benefits The projected compensation range for this position is $135K - $160K annualized (USD). The final salary offered will generally fall within this range and is determined by various factors, including but not limited to the individual's particular combination of education, knowledge, skills, competencies, and experience, as well as internal pay equity, location, contract-specific affordability and other organizational requirements.

• Support enterprise data platform initiatives • Manage and implement changes across enterprise data environments including Azure Synapse and Databricks • Support ETL processes, data pipelines, and API connectivity • Ensure data consistency, quality, and accessibility • Develop and maintain scalable data pipelines and integration services • Collaborate with data architects, analysts, and business stakeholders to support data integration and master data initiatives.

• Support the design, discovery, and implementation of enterprise data platforms that enable advanced analytics and operational workflows. • Lead the development of a centralized data storefront and enterprise analytics ecosystem. • Integrate enterprise data into operational workflows. • Develop and maintain scalable data architectures, pipelines, and data integration frameworks. • Integrate multiple structured and unstructured data sources from enterprise systems, operational databases, and external platforms. • Ensure data architectures support data governance, lineage, quality monitoring, and enterprise data management standards. • Lead technical discovery sessions with mission and business stakeholders to identify data sources, use cases, and operational requirements. • Develop analytics dashboards, operational insights, and data products supporting leadership decision-making. • Provide technical leadership in data architecture design and integration strategies.

• Design, build, and maintain scalable batch and streaming data pipelines supporting enterprise analytics, reporting, and downstream consumption • Develop and optimize data ingestion, transformation, and orchestration workflows across structured and semi‑structured data sources • Engineer and maintain curated, analytics‑ready data models (e.g., dimensional, canonical, or domain‑oriented datasets) • Ensure data solutions meet performance, reliability, availability, and recoverability expectations • Implement data solutions aligned to Penn Mutual’s cloud data platform strategy, including cloud storage, compute, and analytics services • Apply data architecture patterns that support data lakes, lake houses, and analytical warehouses • Partner with Enterprise Architecture to ensure data solutions conform to technology standards, integration patterns, and security requirements • Contribute to platform evolution decisions, including tooling selection, architectural patterns, and modernization initiatives • Embed data quality checks, validation rules, and observability into pipelines to ensure trusted data • Support data governance and stewardship practices, including metadata management, lineage, and controlled data access • Ensure data solutions comply with security, privacy, and regulatory requirements relevant to financial services and insurance • Collaborate with analytics, reporting, and data science teams to enable self‑service analytics and advanced insights • Translate business requirements into well‑designed data structures and datasets that are easy to consume and reuse • Support downstream use cases including dashboards, regulatory reporting, operational analytics, and advanced modeling • Serve as a technical leader and subject‑matter expert for data engineering practices across the organization • Mentor junior and mid‑level data engineers through design reviews, code reviews, and knowledge sharing • Promote engineering best practices including version control, automated testing, CI/CD, and documentation • Drive continuous improvement through evaluation of emerging data technologies and industry trends.