About this role
We are seeking a Data Analyst with strong technical skills and investigative mindset to join our team. This analyst will be responsible for reviewing table-level data discrepancies, identifying likely causes, and working with engineering teams to suggest and validate monitoring or logging improvements within upstream systems.
Key Responsibilities
- Analyze reconciliation delta outputs (inserts, updates, deletes) per table to identify patterns and potential causes of data mismatches between source and target systems.
- Reverse-engineer data flow issues by tracing potential data quality, process timing, or transformation problems in the upstream systems.
- Collaborate with engineers to understand source systems (e.g., Java-based microservices) and how data is written, transformed, or synchronized downstream.
- Identify candidate fields or metadata (e.g., last_updated timestamps, missing keys) that could explain discrepancies.
- Propose actions to improve traceability in source systems, including:
- Instrumenting logs
- Capturing audit fields
- Implementing or enhancing existing data lineage
- Prepare concise documentation and recommendations, including summary reports of analysis findings and proposals for corrective actions.
- Optionally support data validation in both source and target environments to verify resolution of previously identified issues. (inferred)
Required Qualifications
- 4+ years of experience in data analysis, business intelligence, or data engineering roles with strong investigative and reporting capabilities
- Strong proficiency in SQL for querying large datasets
- Hands-on experience analyzing data within AWS environments, including Athena, S3, and Glue
- Working knowledge of Python and/or Java to understand data generation paths and support collaboration with development teams
- Familiarity with microservices architectures and their implications for data persistence and synchronization
- Experience analyzing operational data for root cause and working with developers to close systemic gaps (e.g., missed updates, stale reads)