Job Purpose and Impact
We are building CRR (Corporate Risk Reporting), an enterprise platform delivering critical commodity risk insights. As a QA Engineer, you will be responsible for ensuring the accuracy, reliability, and consistency of data pipelines and risk reporting outputs. You will work closely with data engineers, SQL developers, and Application Developers to validate data flows and ensure alignment with business requirements.
Key Accountabilities
Data & Functional Validation
- Validate end-to-end data flows from source systems (SAP/ODP, CTRM, pricing) into Aurora/PostgreSQL.
- Perform data validation and reconciliation to ensure accuracy, completeness, and consistency.
- Support validation of risk outputs (positions, exposures, pricing, P&L) against defined business logic
Pipeline & Ingestion Testing
- Test ETL/ELT pipelines for correctness, data integrity, and consistency.
- Validate snapshot and delta ingestion processes, including CDC scenarios.
- Verify handling of edge cases such as data ordering, duplication (idempotency), and recovery.
Test Automation & Execution
- Develop and maintain test cases and automated test scripts for data pipelines and SQL transformations.
- Execute functional, regression, and integration testing across data and reporting layers.
- Contribute to test automation frameworks using Python or similar tools.
Performance & EOD Validation
- Support testing of high-volume data processing, especially end-of-day (EOD) workloads.
- Identify data discrepancies and performance issues during peak processing.
Defect Management & Collaboration
- Log, track, and support resolution of defects and data issues.
- Work closely with engineers and analysts to analyze root causes and validate fixes.
- Participate in test planning, sprint activities, and release validation.
Qualifications
- Minimum requirement of 3 years of relevant work experience, typically reflecting 5+ years in QA/testing for data platforms, ETL pipelines, or analytics systems.
- Proven ability to balance hands-on technical contribution and cross-team influence.
- Strong communication skills with the ability to engage effectively with both technical and business stakeholders.
Required Skills:
Mandatory:
- Strong proficiency in SQL for data validation and analysis.
- Experience with ETL/ELT processes and data pipeline testing.
- Understanding of data quality concepts and validation techniques.
- Familiarity with test automation using Python or similar tools.
- Basic understanding of CDC patterns, schema evolution, and data integrity principles.
Good to Have:
- Experience with AWS data services (Glue, Lambda, Step Functions, DMS).
- Knowledge of PostgreSQL/Aurora databases.
- Exposure to event-driven systems (e.g., Kafka).
- Proficient in containerization and orchestration using Docker and Kubernetes
- Familiarity with financial, trading, or risk management systems
