Senior Data Engineer

Project:

Objective of the project is to implement a solution to retain all data / documents needed to satisfy (a) Retention requirements of all Acts and Regulations governing the Credit Cards business (e.g. ECOA, FCRA, EFTA, TILA etc.), (b) GS Privacy Policy and (c) Any litigation and legal holds. The solution should enable retrieval of data/documents to service Regulatory inquiries, Audit requests, Litigation requests, as well as all account documentation requirements from other external parties related to the business.

Role & Responsibilities:

• Design and maintain scalable data pipelines for data ingestion, transformation, and processing.
• Build and optimize ETL/ELT workflows using PySpark, Databricks, SQL, and Airflow.
• Develop data models and solutions for retention, reporting, and analytics needs.
• Ensure data quality, integrity, and reliability through validation and monitoring frameworks.
• Integrate data from multiple sources and optimize processing performance.
• Collaborate with cross-functional teams to deliver data solutions and resolve data issues.
• Maintain technical documentation, data lineage, and mapping artifacts.
• Support production deployments, troubleshooting, and continuous improvement through engineering best practices.

Location: Bengaluru Office; Full time work from office

Technical Skills Requirements:

1. PySpark & Spark SQL: Hands-on experience writing production-grade PySpark code
2. Databricks: Proven experience working within Databricks environments, including cluster configuration and Delta Lake
3. Apache Airflow: Strong experience building and managing pipelines in Apache Airflow
4. SQL Mastery: Advanced SQL skills (window functions, CTEs, query optimization, indexing strategies)
5. Data Modeling: Solid understanding of modern data warehousing concepts
6. Version Control: Proficiency with Git and CI/CD deployment workflows
7. AWS: Experience in Amazon Web Services (AWS) cloud infrastructure
8. ETL/ELT: Proven track record of designing, building, and optimizing scalable ETL/ELT pipelines and data platforms