Role Overview :

We are seeking a detail-oriented and analytical Data Analyst with 3 - 6 years of experience to support our Banking Data Architecture and Analytics division. The ideal candidate will design, develop, and maintain high-impact MIS dashboards, streamline ELT/ETL pipelines, and execute complex analytics across large-scale banking dataset environments.

You should possess strong technical proficiency in Oracle Database (PL/SQL), intermediate Python, Hive, and Linux/Unix environments, combined with an understanding of strict financial data governance, security, and enterprise reporting compliance.

Key Responsibilities :

- Reporting & MIS Analytics : Design, develop, and maintain management information systems (MIS), executive dashboards, and regulatory/business performance reports.

- Data Pipeline Optimization : Develop, execute, and optimize ETL/ELT processes using SQL, Python, Hive, and Elastic environments.

- Data Architecture Support : Build and support Hive-based data solutions to manage large-scale financial data repositories.

- Data Quality & Performance : Proactively analyze, troubleshoot, and resolve complex data quality issues, performance bottlenecks, and database query slowdowns.

- Automation & Scripting : Write efficient, automated SQL/Python scripts within Linux environments to minimize manual workloads and enhance process efficiency.

- Stakeholder Collaboration : Partner closely with business analysts, technology teams, risk managers, and business stakeholders to translate banking business needs into actionable data solutions.

- Lifecycle Management : Actively participate in code reviews, unit testing, optimization, and production deployment cycles.

- Governance & Compliance : Strictly adhere to banking data governance policies, IT security protocols, regulatory frameworks, and enterprise compliance standards.

Technical Skills :

- Oracle Database : Advanced SQL and PL/SQL development, query optimization, execution plan tuning, and performance diagnostics.

- Programming : Intermediate proficiency in Python for data automation and scripting.

- Big Data : Hands-on experience working with Hive data warehouses.

- Operating Systems : Strong Linux/Unix command-line proficiency, job process monitoring, and shell scripting.

Preferred Skills :

- AWS Cloud : Amazon S3, AWS Lambda, and Amazon OpenSearch.

- Domain : Prior experience handling financial dataset structures, credit/risk reports, or banking data lakes.