Staff Data Engineer

Penn Mutual is a company that has empowered individuals and businesses for over 175 years. They are seeking a Staff Data Engineer who will design and build enterprise data platforms and pipelines to support analytics and reporting, while providing technical leadership across various data engineering processes.

Responsibilities:

• 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
• Demonstrates a commitment to AI fluency by embracing AI tools and technologies to enhance individual and team performance, decision-making, and innovation

Requirements:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field (Master's degree preferred)
• 10+ years of professional experience in data engineering, analytics engineering, or data platform development
• Strong proficiency in SQL and at least one modern programming language commonly used for data engineering (e.g., Python, Java, or Scala)
• Extensive experience designing and building data pipelines and analytical data models
• Hands‑on experience with cloud‑based data platforms and distributed data processing concepts
• Solid understanding of data architecture patterns, data integration, and performance optimization
• Strong problem‑solving skills with the ability to analyze complex data challenges and implement effective solutions
• Excellent communication skills, with the ability to explain data concepts to both technical and non‑technical stakeholders
• Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes)
• Experience with AWS serverless integration (e.g., Glue, Lambda, Step)
• Knowledge of Infrastructure as a Service concepts and tooling (Cloud Formation, Terraform, etc.), deployment automation tools (Jenkins, GitHub Actions, Bamboo, etc.)
• Knowledge of software development methodologies such as Agile or Scrum
• Previous experience in leading or mentoring junior engineers