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JOB SUMMARY
The role involves managing and maintaining analytics data marts, designing and optimizing ETL workflows, ensuring high-quality data availability for analytics teams, routine workflow and Report automation and supporting business functions like Risk/ Finance/Collection etc.
QUALIFICATIONS
- Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related field.
- Master’s degree (optional but preferred) in Data Science, Business Analytics, Information Systems, or similar fields.
Professional Experience
- 2–4 years of experience in Data Engineering, ETL Development, Analytics Engineering, or a similar data-focused role.
- Hands-on experience with data warehousing, database management, and ETL tools like SSIS/SSRS etc. and experience in NBFC is preferred
TECHNICAL SKILLS
- Strong experience in Data modelling, ETL development and data pipeline management.
- Proficiency in SQL/Oracle, data warehousing concepts, and relational databases.
- Experience with automation tools and workflow scheduling (e.g. cron jobs, SQL procure etc.).
- Knowledge of data quality frameworks and monitoring practices.
- Ability to collaborate with analytics teams and business stakeholders.
- Attention to detail and strong problem‑solving skills.
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ROLE & RESPONSIBILITY
1. Data Mart Management
- Maintain and enhance the Analytics Data Mart to support organizational reporting and modelling requirements.
- Develop and manage Analytics data mart which include leads, Acquisition, Loan details, Bureau Mart and Collection Variable Mart for Risk and analytics use.
2.ETL Development & Data Pipelines
- Design, build, and maintain ETL (Extract, Transform, Load) processes to extract data from multiple systems and sources.
- Perform daily and monthly data extractions accurately and within defined timelines.
- Transform raw data into structured, analysis-ready datasets.
3. Collaboration & Data Quality
- Work closely with IT team and data analysts to ensure consistent access to clean, reliable, and well‑organized data.
- Validate and monitor data quality, completeness, and accuracy across systems.
4. Infrastructure Optimization
- Monitor and optimize data processes and infrastructure to ensure performance, scalability, and efficiency as data volumes grow.
- Implement best practices for data governance, documentation, and version control.
5. Automation & Monitoring
- Automate routine data workflows and reporting processes to improve efficiency.
- Implement monitoring mechanisms to proactively detect performance issues, errors, and data anomalies.
6. Reporting & Analysis
- Generate reports for Business and collection like alternate address and contact information for delinquent cases sourced from bureaus.
- Provide actionable insights to support Business/ collections strategy and performance.
- Prepare ad hoc reports and conduct analysis as per business requirements.
- Support to cross-functional teams with timely insights and data extracts.
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