Senior Data Engineer - ETL

Key Responsibilities :

- Design, build, and maintain scalable data pipelines for processing financial data across functions like loan origination, disbursement, collection, and regulatory reporting.

- Develop and optimize ETL workflows to extract data from core banking systems, loan management platforms, payment gateways, credit bureaus, and other sources.

- Perform complex data blending and transformation to merge internal financial datasets with external credit scores, market data, and compliance-related information.

- Ensure data quality, consistency, and regulatory compliance through robust transformation logic and validations.

- Build and maintain data warehouse architectures, including cloud-based solutions (e.g., Snowflake, Redshift, BigQuery).

- Collaborate closely with teams in finance, risk management, underwriting, and compliance to align data models and outputs with business needs.

- Implement data validation frameworks and monitoring systems to guarantee data accuracy for internal use and regulatory reporting.

- Tune and optimize query performance for large-scale datasets, improving data accessibility and processing speed.

Technical Skills :

- Proficiency in SQL, Python, and/or Scala for advanced data processing.

- Hands-on experience with ETL tools like Informatica, Talend, SSIS, or similar.

- Experience with modern data warehousing platforms such as : Snowflake, Amazon Redshift, Google BigQuery, or traditional enterprise DWH systems.

- Knowledge of big data technologies : Hadoop, Apache Spark, Kafka.

- Working experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.

Domain Knowledge :

- Strong understanding of financial products and the loan lifecycle from origination to closure.

- Familiarity with loan origination systems, collections processes, and credit risk evaluation.

- Knowledge of financial compliance and regulatory frameworks such as : RBI guidelines, CIBIL data formats, and other industry standards.

Preferred Qualifications :

- Experience in real-time data streaming and event-driven data architectures.

- Familiarity with data governance frameworks, data lineage, and metadata management tools.

- Certifications in cloud platforms (AWS, Azure, GCP) or data engineering tools.

- Understanding of machine learning workflows related to credit scoring or risk assessment.