Analytics Engineer

• Perform ongoing performance monitoring of BSA/AML systems, including alerts, scenarios, agents and customer risk rating models.
• Analyze alert volumes, disposition outcomes, conversion rates, and false-positive trends.
• Identify performance degradation, threshold issues, or emerging risk requiring tuning or escalation.
• Develop and maintain KPIs and metrics to demonstrate system effectiveness.
• Conduct testing of new or modified agents, alerts, thresholds, and risk rating changes.
• Support validation of vendor system updates, enhancements, and new services.
• Assist with model change impact analysis and pre-/post implementation reviews.
• Document testing methodologies, results, and approvals in accordance with governance standards.
• Use data analytics to identify trends, anomalies, and emerging typologies.
• Produce management and board-level reporting on BSA system performance and risk exposure.
• Create dashboards and automated reports to support ongoing monitoring.
• Respond to ad-hoc data requests from BSA, internal audit, and others.
• Support BSA/AML model governance programs, including periodic reviews and independent validations.
• Maintain clear documentation and audit trails that support regulatory examinations.
• Assist with examiner and auditor inquiries related to system performance and methodology.
• Ensure monitoring practices align with regulatory guidance, FFIEC expectations, and internal policies.
• Partner with various risk, compliance and data teams to resolve system or data issues.
• Participate in vendor meetings related to performance, enhancements, and roadmap planning.
• Provide analytical support for enterprise-level risk assessments and program enhancements.

BSA Data Analytics Specialist

• Develop and maintain ETL processes to support data ingestion, transformation, and integration.
• Work with various file formats (TXT, CSV, XML, JSON, YAML) to ensure seamless data processing.
• Implement and optimize ETL workflows using EasyMorph, Databricks, Azure Data Factory, Talend, and Apache Nifi.
• Write clean, efficient, and maintainable code using Python, C#, or Java.
• Ensure data quality, accuracy, and integrity across ETL pipelines.
• Collaborate with data engineers and analysts to define and refine ETL requirements.
• Troubleshoot and resolve ETL failures, ensuring minimal downtime and impact.
• Follow test-driven development (TDD) methodologies to improve reliability and maintainability.
• Maintain and document ETL processes, providing insights and recommendations for improvements.

Associate ETL Engineer

• Lead the end-to-end delivery of complex data engineering projects, including requirements definition with business stakeholders, technical design, implementation oversight, and delivery coordination with data engineering, analytics, and AI/ML teams
• Serve as the primary technical liaison between business partners and engineering teams, translating business requirements into well-scoped, executable data initiatives with clear outcomes
• Architect and develop cloud-native data solutions on AWS, including the design and deployment of data pipelines using AWS Glue, Airflow (Amazon MWAA), Step Functions, Lambda, and related services
• Lead the migration and modernization of existing data workflows, including the conversion of RPA-based automation into cloud-native orchestration solutions using AWS Airflow
• Design, develop, and deploy RESTful and authenticated APIs on Kubernetes (K8s) to expose data services and other reusable data products consumed by downstream systems
• Oversee and contribute to infrastructure-as-code practices using Terraform, ensuring analytics and data engineering workloads are provisioned, managed, and repeatable across environments
• Establish and enforce data pipeline best practices including data quality checks, lineage tracking, and governance standards across all projects under this role's scope
• Monitor, observe, and maintain production data systems and pipelines, using tools such as CloudWatch, DataDog, or Grafana as applicable
• Conduct exploratory and operational data analysis to surface deficiencies, validate pipeline outputs, and support data-driven decision making for business stakeholders
• Define and communicate data architecture standards and patterns to both technical and non-technical audiences, including documentation of architectural decisions and pipeline design
• Collaborate on cross-cutting concerns such as data security, infrastructure alignment, and platform strategy
• Coordinate with DevOps and infrastructure teams to ensure that data engineering deliverables align with CI/CD pipelines, containerization standards, and broader organizational engineering practices

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