Staff Data Engineer

Penn Mutual•Philadelphia, PA

35d

About The Position

The Staff Data Engineer is responsible for designing, building, and evolving Penn Mutual’s enterprise data platforms and pipelines that enable analytics, reporting, and data‑driven decision making. This role provides senior technical leadership across data ingestion, transformation, storage, and consumption layers, ensuring data is reliable, scalable, secure, and well‑governed. As a senior individual contributor, the Staff Data Engineer partners closely with architecture, analytics, data governance, and application teams to translate business and analytical needs into robust data engineering solutions aligned with Penn Mutual’s Connected Data Strategy and following cloud and enterprise technology standards established by the Enterprise Architecture group.

Requirements

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the minimum knowledge, skill, and/or ability required.
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.

Nice To Haves

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.

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