Data Analyst / Data Mapping Analyst (AML & Fraud Platforms)Overview / Summary We are seeking a Data Analyst with strong data mapping, SQL, and ETL knowledge to support data-impacting changes across Fraud and AML platforms. This role involves analyzing data requirements, creating source-to-target mappings, assessing platform impacts, collaborating with business and technical stakeholders, and supporting data integrity, regulatory reporting, and data onboarding initiatives.
Key Responsibilities - Review and analyze data-impacting changes across Fraud and AML platforms to understand source systems, downstream impacts, and business implications.
- Read and interpret detailed data mapping documents and functional specifications.
- Develop and maintain ETL-related solutions and support enhancements to existing ETL frameworks.
- Collaborate with data architects, business analysts, compliance teams, data engineers, source system owners, and business stakeholders.
- Gather data requirements and translate them into clear, actionable mapping documents.
- Create and maintain source-to-target data mappings, including documented transformation logic and acceptance criteria.
- Write detailed JIRA stories covering business value, mapping changes, data onboarding activities, and expected platform impacts.
- Assess the impact of new data sources, product changes, and business enhancements on screening and detection platforms.
- Coordinate with AML compliance teams to ensure data integrity and regulatory reporting requirements are met.
- Hand off refined requirements to scrum teams and provide clarification during development and testing.
- Identify and escalate data quality issues at source systems.
- Support User Acceptance Testing (UAT) by answering questions regarding expected data behavior and mapping logic.
- Work across multiple scrum teams simultaneously while prioritizing data-related initiatives.
- Write complex SQL queries, stored procedures, and PL/SQL blocks used within ETL workflows.
- Perform query performance tuning and work with explain plans and indexing strategies.
- Participate in code reviews and technical walkthroughs.
- Maintain technical documentation for developed and modified ETL jobs.
- Support adoption of AI-assisted tools and techniques in data pipeline development and anomaly detection.
Required Qualifications - Hands-on SQL experience.
- Ability to write complex SQL queries, stored procedures, and PL/SQL blocks.
- Experience with query performance tuning, explain plans, and indexing strategies.
- Experience reading and interpreting data mapping documents and functional specifications.
- Strong understanding of dimensional modeling, star schema, and snowflake schema design.
- Experience with Slowly Changing Dimensions (SCDs) and ETL best practices.
- Knowledge of large-scale financial data processing environments and data warehouse optimization.
- Experience creating and maintaining source-to-target data mappings.
- Experience gathering and documenting business and data requirements.
- Experience assessing impacts of data changes across platforms.
- Experience working with multiple scrum teams and stakeholders.
- Strong communication skills.
- Exposure to AI-assisted tools for code generation, data validation, or anomaly detection.
- Highly Desirable
- AML domain knowledge.
- Fraud domain knowledge.
Good to Have - Knowledge of AI tools and their usage.
- Data visualization knowledge.
- ETL architecture and workflow knowledge.
- Client product knowledge.
- AML or Fraud business knowledge.
