Python Data Analyst (BA + QA )

AI overview

Support data management initiatives in financial services through data validation, quality assurance, and collaboration with stakeholders to enhance data accuracy and reliability.

📍 Role Overview

We are seeking a Data Management Analyst with a strong blend of Business Analysis and Quality Assurance experience to support data management initiatives within the financial domain. The ideal candidate will have hands-on exposure to data warehouses, data quality, and reconciliation processes, and will work closely with business and technical stakeholders to ensure data accuracy, consistency, and reliability.

🎯 Key Responsibilities

Act as a bridge between business users and technical teams to understand data requirements and translate them into functional specifications.
Perform data validation, reconciliation, and quality checks, including implementation, monitoring, and issue resolution.
Analyze data from data warehouses and source systems to identify gaps, inconsistencies, and improvement opportunities.
Design and execute data quality test cases, support UAT, and ensure adherence to business rules.
Manage SLA-driven support activities, including handling queries and issues raised by end users.
Prepare and maintain functional and technical documentation such as BRDs, FRDs, data mapping documents, and SOPs.
Collaborate with stakeholders to support data governance, controls, and continuous improvement initiatives.
Participate in project discussions, requirement walkthroughs, defect triage, and data review sessions.

✅ Required Qualifications & Experience

Bachelor’s degree in Computer Science, Information Technology, or related field.
3–7 years of experience in Data Management, preferably within the financial services industry.
Strong experience in Data Warehouse environments (data validation, reporting layers, reconciliation).
Proven background in Business Analysis and/or Data Quality/QA roles.
Hands-on experience with data analysis and reconciliation activities.
Working knowledge of SQL and Python for data analysis and validation (not heavy development).
Experience handling end-user queries and SLA-based support.
Strong experience in documentation (functional specs, data definitions, test scenarios, reports).
Excellent problem-solving, communication, and stakeholder management skills.
High attention to detail, data accuracy, and quality.
Ability to manage multiple priorities in a fast-paced environment.

🌟 Nice to Have

Experience with banking/financial data platforms.
Exposure to data governance, metadata management, or regulatory reporting.
Prior involvement in UAT or data migration/warehouse projects.