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Aml Model Validation Jobs in Utah (NOW HIRING)

Utah Community Credit Union

Data Engineer

Provo, UT · On-site

$108K - $130K/yr

... validation rules, and alerting for pipeline anomalies • Lead the implementation of data modeling ... BSA/AML requirements, and GLBA data privacy standards • Partner with the IT security team to ...

Utah Community Credit Union

Data Engineer

Provo, UT · On-site

$108K - $130K/yr

... validation rules, and alerting for pipeline anomalies • Lead the implementation of data modeling ... BSA/AML requirements, and GLBA data privacy standards • Partner with the IT security team to ...

Lendingclub Corporation

Compliance Audit Manager

Lehi, UT · On-site

$94K - $125K/yr

Partner with business leaders to validate findings, identify root causes, and agree on practical ... We utilize a hybrid work model, and our teams are in-office Tuesdays, Wednesdays, and Thursdays. In ...

All JobsAml Model Validation JobsAml Model Validation Jobs in Utah

Aml Model Validation information

What are the key skills and qualifications needed to thrive as an AML model validation analyst, and why are they important?

To excel in AML Model Validation, you typically need a strong background in quantitative analysis, statistics, and experience with anti-money laundering regulations, often supported by a degree in finance, mathematics, or a related field. Familiarity with statistical software (such as SAS, R, or Python), model validation frameworks, and knowledge of regulatory guidelines like those from the OCC or FFIEC are important. Strong analytical thinking, attention to detail, and clear communication skills set outstanding professionals apart in this role. These competencies are crucial for ensuring AML models are accurate, compliant, and effective in detecting suspicious financial activities.

What is AML model validation?

AML model validation is the process of evaluating and testing anti-money laundering (AML) models to ensure they are accurate, effective, and compliant with regulatory standards. This involves examining the model’s design, data inputs, performance metrics, and overall effectiveness in detecting suspicious activities. Regular validation helps to identify weaknesses, reduce false positives or negatives, and ensure that the model adapts to evolving risk scenarios. Financial institutions are required by regulators to validate their AML models regularly to mitigate risks and maintain robust compliance programs.

What are some common challenges faced by professionals in AML model validation roles, and how can they be addressed?

Professionals in AML Model Validation often encounter challenges such as ensuring models remain effective against evolving financial crime techniques and managing the complexity of regulatory expectations. They must regularly update and back-test models to address changes in transaction patterns and compliance requirements, which can be resource-intensive. Collaboration with data scientists, risk management teams, and compliance officers is crucial for interpreting results and implementing improvements. Staying current with regulatory guidance and industry best practices helps address these challenges and supports career advancement in this dynamic field.

What is the difference between Aml Model Validation vs Aml Analyst?

AspectAml Model ValidationAml Analyst
CertificationsAML certifications, model validation trainingAML certifications, compliance training
Work EnvironmentModel validation teams, risk management departmentsCompliance departments, financial institutions
Primary FocusValidating AML models, ensuring accuracy and effectivenessMonitoring transactions, investigating suspicious activities
Industry UsageFinancial institutions, banks, fintechsFinancial institutions, banks, regulatory agencies

While both roles operate within AML frameworks, Aml Model Validation focuses on testing and validating AML models to ensure they work effectively, whereas Aml Analysts handle daily transaction monitoring and investigations. The validation role emphasizes model accuracy and compliance, while analysts focus on detecting and reporting suspicious activities.

What are popular job titles related to Aml Model Validation jobs in Utah? For Aml Model Validation jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Aml Model Validation jobs in Utah look for? The top searched job categories for Aml Model Validation jobs in Utah are:
What cities in Utah are hiring for Aml Model Validation jobs? Cities in Utah with the most Aml Model Validation job openings:
Aml Model Validation jobs near you

Data Engineer

Utah Community Credit Union

Provo, UT • On-site

$108K - $130K/yr

Full-time

Posted 8 days ago

Job description

The Data Engineer will own the design, development, and optimization of the data infrastructure that underpins analytics, reporting, and machine learning initiatives across the credit union. Working closely with business intelligence analysts, compliance teams, and technology leadership, this role requires both technical depth and the ability to translate complex data challenges into reliable, scalable solutions within a regulated financial environment.

ESSENTIAL FUNCTIONS AND BASIC DUTIES

• Architect, build, and maintain robust ETL/ELT pipelines to integrate data from core banking systems, lending platforms, digital banking channels, and third-party vendors

• Design and manage the credit union’s data warehouse and data lake, including schema design, partitioning strategies, and performance optimization

• Develop and enforce data quality frameworks, including automated testing, validation rules, and alerting for pipeline anomalies

• Lead the implementation of data modeling best practices (dimensional modeling, data vault, or similar) to support scalable analytics

• Collaborate with data analysts, compliance officers, and business stakeholders to define data requirements and deliver trusted data products

• Manage orchestration and scheduling of data workflows using tools such as Matillion, Snowflake, or equivalent

• Evaluate, implement, and maintain cloud data infrastructure on Snowflake or Azure, including compute, storage, and networking resources

• Ensure all data processes comply with applicable regulations, including NCUA guidelines, BSA/AML requirements, and GLBA data privacy standards

• Partner with the IT security team to enforce data access controls, encryption standards, and audit logging

• Mentor junior data engineers and analysts, providing technical guidance and code reviews

• Drive adoption of DataOps practices, including CI/CD for data pipelines, version control, and documentation standards

• Support data migration efforts during platform transitions, core system upgrades, or mergers and acquisitions

• Works a regular and predictable schedule. 

QUALIFICATIONS

REQUIRED QUALIFICATIONS

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field (or equivalent professional experience)

• 3–5 years of hands-on experience in data engineering, data infrastructure, or a closely related role

• Advanced proficiency in SQL and experience with large-scale relational and columnar databases (e.g., PostgreSQL, Redshift, Snowflake, BigQuery)

• Strong Python skills for data pipeline development and automation

• Demonstrated experience designing and maintaining ETL/ELT workflows using tools such as dbt or Matillion

• Working knowledge of at least one major cloud platform (Snowflake, AWS, Azure, or GCP) and associated data services

• Solid understanding of data warehousing concepts including dimensional modeling and schema design

• Experience implementing data quality monitoring, lineage tracking, and observability practices

• Strong communication skills with the ability to work effectively across technical and non-technical audiences

PREFERRED QUALIFICATIONS

• Experience in a regulated financial services environment such as credit unions, banks, or fintech companies

• Familiarity with credit union core banking platforms such as Symitar (Episys), Jack Henry, or FiServ

• Knowledge of data governance frameworks, or master data management

• Exposure to machine learning pipelines and feature engineering for predictive models

• Experience with BI platforms such as Power BI, Tableau, or Looker

• Relevant certifications such as AWS Certified Data Analytics, Snowflake SnowPro, or dbt Certified Developer

• Familiarity with NCUA examination processes or financial regulatory reporting (e.g., HMDA, Call Report data)

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