BankUnited, Inc., with total consolidated assets of $35.0 billion at December 31, 2025, is a bank holding company with one wholly owned subsidiary, BankUnited.

BankUnited (NYSE: BKU) is a national bank headquartered in Miami Lakes, Florida with banking centers in Florida, the New York metropolitan area and in Dallas, Texas. We pride ourselves on our entrepreneurial and collaborative culture encompassing the best minds, the brightest talent and the boldest decision makers.

BankUnited is ranked #4 as one of America’s Most Trusted Companies in the Banking industry and is honored to have been included on the Newsweek and Statista America’s Most Trusted Companies Award List!

At BankUnited, we foster an inclusive environment where all employees have the opportunity to advance, grow and achieve their goals. Our rally cry is to GO FOR MORE™, a call to action to go above and beyond to provide the best customer experience to every client.

SUMMARY: 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.

ESSENTIAL DUTIES AND RESPONSIBILITIES include the following:

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.

SUPERVISORY RESPONSIBILITIES:

Supervises function, projects or services and/or one or more employees, as applicable.

Carries out supervisory responsibilities in accordance with the organization's policies and applicable laws.

Responsibilities include interviewing, hiring, and training employees; planning, assigning, and directing work; appraising performance coaching; rewarding and disciplining employees; addressing complaints and resolving problems.

EDUCATION:

Bachelor's Degree in a quantitative or technical field (eg, Computer Science, Mathematics, Engineering, Economics, or Statistics) required Master's Degree preferred

EXPERIENCE:

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

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

KNOWLEDGE, SKILLS AND ABILITIES:

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.