Oracle Database Administrator/MongoDB
PNC Financial
- Location
- PA Pittsburgh 15222
- Work model
- On-Site
- Level
- Mid
- Posted
- 7h ago
Skills
About this role
Position
Overview At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company’s success. As a Data Engineer within PNC's Technology organization, you will be based in Dallas, TX, Pittsburgh, PA, Cleveland, OH, Birmingham, AL or Denver, CO.
Key Responsibilities
Serve as the functional DBA for Oracle and MongoDB across production and non‑production environments. Partner with application teams on schema design, data modeling, and query optimization. Analyze and resolve database-related functional issues, performance bottlenecks, and data anomalies. Support release activities by validating database changes, scripts, and backward compatibility. Provide guidance on transaction management, indexing strategy, and data access patterns. Ensure data consistency, integrity, and correctness across applications and integrations. Review and approve database changes for functional risk and impact. Assist incident response by diagnosing database behavior and data-related failures. Maintain functional documentation, standards, and runbooks for database usage. Core Skills & Expertise: Strong functional expertise in Oracle SQL, PL/SQL, and query tuning. Working knowledge of MongoDB data models, indexing, and query performance. Clear understanding of RDBMS vs NoSQL tradeoffs and use cases. Ability to translate business and application requirements into efficient database designs. Experience supporting enterprise-scale, data-intensive applications. Operating Style Analytical, detail‑oriented, and risk‑aware. Strong collaboration with engineering, QA, and support teams. Focused on prevention, clarity, and sustainable data solutions. PNC is an in-office company that fosters a supportive culture where employees can thrive and achieve balance. We encourage candidates to connect with their recruiter and hiring manager to understand workplace expectations and ensure the role aligns with their goals. PNC will not provide sponsorship for employment visas or participate in STEM OPT for this position.
Job Description
Develops, supports and implements data services for multiple applications to meet business objectives and user requirements. Uses technical knowledge and industry experience to design, build and maintain technology solutions. Works closely with users, developers, operations and business partners to define data service requirements and the data preparation process development. Designs and builds data service infrastructure on multiple data platforms, according to key business processes and the overall workflow. Develops and implements data solutions for multiple applications to ensure its scalability, availability and maintainability. Implements data migration and transformation activities/processes to ensure the accuracy and security of data solutions. PNC Employees take pride in our reputation and to continue building upon that we expect our employees to be: Customer Focused - Knowledgeable of the values and practices that align customer needs and satisfaction as primary considerations in all business decisions and able to leverage that information in creating customized customer solutions. Managing Risk - Assessing and effectively managing all of the risks associated with their business objectives and activities to ensure they adhere to and support PNC's Enterprise Risk Management Framework.
Qualifications
Successful candidates must demonstrate appropriate knowledge, skills, and abilities for a role. Listed below are skills, competencies, work experience, education, and required certifications/licensures needed to be successful in this position.
Preferred Skills
Analytical Thinking, Competitive Advantages, Data Analytics, Data Mining, Data Science,