Data Analyst Senior/Business Analyst Senior - Data and Automation (SQL, Python, Data Quality, AI Tools)

PNC Bank•Strongsville, OH

7d•Onsite

About The Position

Support regularly occurring data provisioning for Commercial model monitoring, model development, third parties, and other analytics. Work closely with data scientists, model analytics management, and downstream consumers to understand and fulfill evolving data requirements. Support onboarding and validation of new data domains (e.g., Marketing) into enterprise analytics environments (e.g., ACZ Bronze), including coverage analysis, reconciliation, and readiness assessment. Perform hands‑on data validation and user acceptance testing (UAT) to ensure datasets are complete, accurate, well‑understood, and fit for use by modeling and analytics stakeholders. Support acquisition‑related data efforts (e.g., historical data ingestion, point‑in‑time and full‑history datasets) and contribute to repeatable frameworks that can be applied to future acquisitions. Streamline and refactor existing code using best practices in SQL and Python to enhance performance, scalability, and maintainability across platforms. Translate and modernize legacy data processes (e.g., SAS‑based workflows) into cloud‑ and big‑data‑native solutions (e.g., SQL, PySpark), including effective use of AI‑assisted tooling to accelerate and validate code translation. Develop new scripts and processes to automate repetitive tasks, reduce manual effort, and improve testing and validation efficiency. Conduct thorough data quality assessments and clearly articulate data anomalies, risks, and constraints to management and data consumers using root‑cause analysis and trend reporting. Partner with stakeholders to resolve data quality issues and implement preventative controls to reduce recurrence. Adhere to change management practices to ensure that all code and process changes are reviewed, tested, documented, and approved prior to implementation. Track and document changes to data processes, validation results, and decisions to support auditability, transparency, and knowledge transfer. Occasionally participate in scrum or agile development teams, supporting Enterprise Data Management and analytics initiatives through planning, development, testing, and delivery activities. 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.

Requirements

University / college degree, with 3+ years of relevant / direct industry experience.
In lieu of a degree, a comparable combination of education, job specific certification(s), and experience (including military service) may be considered.

Nice To Haves

Analytical Thinking
Competitive Advantages
Data Analytics
Data Mining
Data Science
Machine Learning (ML)
Python (Programming Language)
Structured Query Language (SQL)

Responsibilities

Support regularly occurring data provisioning for Commercial model monitoring, model development, third parties, and other analytics.
Work closely with data scientists, model analytics management, and downstream consumers to understand and fulfill evolving data requirements.
Support onboarding and validation of new data domains (e.g., Marketing) into enterprise analytics environments (e.g., ACZ Bronze), including coverage analysis, reconciliation, and readiness assessment.
Perform hands‑on data validation and user acceptance testing (UAT) to ensure datasets are complete, accurate, well‑understood, and fit for use by modeling and analytics stakeholders.
Support acquisition‑related data efforts (e.g., historical data ingestion, point‑in‑time and full‑history datasets) and contribute to repeatable frameworks that can be applied to future acquisitions.
Streamline and refactor existing code using best practices in SQL and Python to enhance performance, scalability, and maintainability across platforms.
Translate and modernize legacy data processes (e.g., SAS‑based workflows) into cloud‑ and big‑data‑native solutions (e.g., SQL, PySpark), including effective use of AI‑assisted tooling to accelerate and validate code translation.
Develop new scripts and processes to automate repetitive tasks, reduce manual effort, and improve testing and validation efficiency.
Conduct thorough data quality assessments and clearly articulate data anomalies, risks, and constraints to management and data consumers using root‑cause analysis and trend reporting.
Partner with stakeholders to resolve data quality issues and implement preventative controls to reduce recurrence.
Adhere to change management practices to ensure that all code and process changes are reviewed, tested, documented, and approved prior to implementation.
Track and document changes to data processes, validation results, and decisions to support auditability, transparency, and knowledge transfer.
Occasionally participate in scrum or agile development teams, supporting Enterprise Data Management and analytics initiatives through planning, development, testing, and delivery activities.

Benefits

medical/prescription drug coverage (with a Health Savings Account feature)
dental and vision options
employee and spouse/child life insurance
short and long-term disability protection
401(k) with PNC match
pension and stock purchase plans
dependent care reimbursement account
back-up child/elder care
adoption, surrogacy, and doula reimbursement
educational assistance, including select programs fully paid
a robust wellness program with financial incentives
maternity and/or parental leave
up to 11 paid holidays each year
9 occasional absence days each year, unless otherwise required by law
between 15 to 25 vacation days each year, depending on career level; and years of service.