IT Engineer Principal II -Data & Analytics

PSEG•Newark, NJ

2d•$121,200 - $199,200•Hybrid

About The Position

The Principal 2 IT Engineer – Data & Analytics plays a key role in delivering and maintaining the data assets required for enterprise reporting, analytics, automation, and AI initiatives. The role focuses on designing, building, optimizing, and supporting data pipelines, curated datasets, and related data engineering components that serve analysts, data scientists, and business users across the company. This engineer participates in design, development, testing, deployment, and production support of data solutions, working closely with business analysts, product managers, and technical partners to ensure reliability, quality, and consistent delivery.

Requirements

Bachelor’s degree in Computer Science or a related technical field.
8–12 years of experience delivering technology solutions, preferably with a focus on data engineering, analytics engineering, or data integration.
Demonstrated leadership through ownership of technical solutions or project components.
Experience documenting technical solutions, data flows, and system behavior.
Foundational knowledge of data management practices and familiarity with data warehouse and data lake architectures.
Strong ability to design, build, and manage data pipelines including transformations, data modeling, metadata, and workload management.
Experience with AWS and/or Microsoft analytics platforms and related data services.
Proficiency with SQL, PL/SQL, and experience working with relational and non‑relational databases.
Experience supporting DevOps practices such as version control, automated testing, and release processes.
Experience collaborating with BI and advanced analytics teams and supporting their data needs.
Experience developing and maintaining ETL/ELT pipelines using tools such as SSIS, AWS Glue, Azure Data Factory, or similar.
Compliance with the Department of Energy's regulation 10 CFR 810 is required.

Nice To Haves

Experience supporting machine learning workflows, including feature pipelines or model deployment.
Familiarity with emerging analytics and AI technologies, including generative AI or retrieval‑based approaches.
Experience with data governance, data quality frameworks, or responsible AI practices.
Exposure to modern data engineering or analytics tools and techniques.

Responsibilities

Analyze business and end‑user requirements and design, configure, develop, and test the data pipelines, transformations, and data models needed for reporting, analytics, and operational use cases.
Create, maintain, and optimize data pipelines as workloads move from development into production, ensuring they perform reliably and efficiently.
Develop and maintain documentation such as data flow diagrams, technical specifications, and process descriptions to support understanding, maintainability, and knowledge transfer.
Work closely with business analysts, BI developers, data scientists, and other data consumers to refine data needs and ensure solution outputs meet functional expectations.
Coordinate with enterprise architects, cloud and infrastructure teams, software development teams, and cybersecurity to ensure data engineering solutions align with enterprise standards and integrate appropriately with other technology areas.
Participate in design reviews and change reviews, offering input to help ensure quality, consistency, and conformance with established engineering practices.
Provide support for production data pipelines and analytics systems, including troubleshooting issues, implementing fixes, performing proactive maintenance, and conducting root‑cause analysis as needed.
Assist with controlled deployments, upgrades, updates, and enhancements following standard change‑management processes and ensuring readiness for production.
Review existing tools, platforms, and processes to evaluate performance, scalability, and alignment with business needs, making recommendations for improvements when appropriate.
Contribute to ongoing data management practices, including metadata management, data quality checks, governance adherence, and structured documentation.
Support continuous improvement of engineering methods, coding standards, automation opportunities, and collaboration practices that strengthen the data engineering discipline.