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 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 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 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.

(ref : hirist.tech)