Data Engineering & Analytics, Credit Review

About the position The Data Engineer supports the Credit Review function through the design, maintenance, and enhancement of the data environment that underpins credit risk analytics and reporting across the bank's lending portfolios. This role combines technical depth in SQL, Python, and AWS with strong analytical judgment in a credit and risk management context. The Data Engineer will maintain and evolve existing data pipelines and dashboards that enable Credit Review's monitoring and analytics programs. The role requires hands-on experience developing and optimizing data solutions in AWS using tools such as Glue, Lambda, Step Functions, and Redshift. The ideal candidate is comfortable maintaining code, querying data, and supporting the ongoing buildout of a scalable, governed analytics environment. This position reports to the Credit Review Data Analytics Manager and works closely with Credit Review leadership, data engineering, and risk analytics teams across the bank. Experience in banking, especially in risk management, is highly valued. Financial risk model development/assurance and/or AI tool-building experience is a differentiator.

Responsibilities

• Troubleshoot, maintain and enhance existing data pipelines, queries, and scripts supporting Credit Review's AWS-based data environment.
• Write and optimize SQL and Python code for data extraction, transformation, and analysis.
• Support data validation, documentation, and change control to ensure data quality and reproducibility.
• Reconcile quarterly department reporting with other Bank sources of reporting to ensure alignment and to flag and escalate where disconnects exist
• Partner with internal stakeholders to deliver ad hoc and recurring analytics to support credit risk identification and monitoring.
• Create, refine, and maintain dashboards and reports used to inform senior management and the Board.
• Assist in developing new data models and tools to enhance Credit Review's monitoring coverage and automation.
• Collaborate with technology partners to ensure alignment with enterprise data architecture, security, and governance standards.
• Participate in periodic internal and regulatory exams by preparing and validating datasets used for testing and analytics.
• Stay current on emerging data tools and techniques that can improve Credit Review's analytical efficiency and insight generation.
• Adheres to and complies with applicable, federal and state laws, regulations and guidance, including those related to anti-money laundering (i.e. Bank Secrecy Act, US PATRIOT Act, etc.).
• Adheres to Bank policies and procedures and completes required training.
• Identifies and reports suspicious activity.

Requirements

• Minimum 3 years of relevant experience in data engineering, data analytics, or quantitative analysis - preferably within financial services, banking, or risk management required
• Hands-on experience with AWS data tools (eg, S3, Glue, Lambda, Step Functions, Athena, Redshift) required
• Strong command of SQL and Python; ability to read, maintain, and enhance production code required
• Familiarity with version control (GitHub) and cloud development workflows preferred
• Knowledge of data analytics practices and concepts (CAAT, trend analysis, data visualization, regression analysis).
• Ability to perform challenging data queries, data mining, reconcile data, analyze results, and form conclusions for Credit Review and senior management.
• Knowledge of commercial and retail loan portfolios, reporting, and analysis techniques.
• Collaborative and team-oriented, with an appreciation for data governance, data quality and risk management principles.
• Detail-oriented, organized, and self-directed with a strong sense of ownership.
• Excellent written and verbal communication skills, including the ability to explain technical concepts to non-technical audiences.
• Ability to work independently and prioritize tasks in a fast-paced environment.
• Bachelor's Degree in a quantitative or technical field (eg, Computer Science, Mathematics, Engineering, Economics, or Statistics) required

Nice-to-haves

• Experience with visualization tools (especially QuickSight) a plus preferred
• Exposure to credit or financial risk models (eg, PD/LGD, CECL, stress testing) is a differentiator preferred
• Experience developing or supporting AI/ML or automation tools is a strong plus preferred
• Master's Degree preferred

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