• 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.