We are looking for a skilled and results-driven Senior Data Engineer with a strong background in the banking domain to join our team. In this role, you will design, build, and optimize scalable data pipelines, data lakes, and analytical architectures on AWS. You will work closely with data scientists, analysts, and banking stakeholders to ensure high-performance data delivery, regulatory compliance, and robust data governance.

Key Responsibilities

• Data Pipeline Development: Design, build, and maintain robust, scalable ETL/ELT data pipelines using PySpark and AWS Glue to process large volumes of structured and unstructured banking data.
• Workflow Orchestration: Implement, monitor, and optimize complex data workflows and scheduling using Apache Airflow.
• Cloud Architecture & Optimization: Leverage core AWS services (such as S3, Redshift, Lambda, RDS, and IAM) to architect secure, cost-effective, and high-availability data solutions.
• Banking Domain Integration: Handle sensitive financial and transactional data with strict adherence to banking security, data privacy, and auditing standards.
• Performance Tuning: Optimize PySpark jobs, SQL queries, and AWS Glue execution times to handle massive data loads efficiently.
• Collaboration: Partner with cross-functional teams including data governance, risk, and analytics to deliver high-quality data assets for business reporting and machine learning initiatives.
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Qualifications & Technical Requirements

• Experience: Minimum 7 years of total hands-on experience in data engineering, data warehousing, or big data solutions, with demonstrated experience in the banking or financial services domain.
• Cloud Computing: Strong expertise in AWS ecosystem and cloud data architectures.
• Big Data & Processing: Advanced proficiency in PySpark for distributed data processing.
• ETL & Integration: Deep practical experience with AWS Glue for serverless data integration and ETL jobs.
• Orchestration Tools: Proven track record of managing workflow scheduling and dependency management using Apache Airflow.
• Database Skills: Strong SQL skills and experience working with relational and columnar databases (e.g., PostgreSQL, Snowflake, AWS Redshift).
• Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related quantitative field.
• 
  
• Skills: aws,data solutions,data

We are looking for a skilled and results-driven Senior Data Engineer with a strong background in the banking domain to join our team. In this role, you will design, build, and optimize scalable data pipelines, data lakes, and analytical architectures on AWS. You will work closely with data scientists, analysts, and banking stakeholders to ensure high-performance data delivery, regulatory compliance, and robust data governance.

Key Responsibilities

• Data Pipeline Development: Design, build, and maintain robust, scalable ETL/ELT data pipelines using PySpark and AWS Glue to process large volumes of structured and unstructured banking data.
• Workflow Orchestration: Implement, monitor, and optimize complex data workflows and scheduling using Apache Airflow.
• Cloud Architecture & Optimization: Leverage core AWS services (such as S3, Redshift, Lambda, RDS, and IAM) to architect secure, cost-effective, and high-availability data solutions.
• Banking Domain Integration: Handle sensitive financial and transactional data with strict adherence to banking security, data privacy, and auditing standards.
• Performance Tuning: Optimize PySpark jobs, SQL queries, and AWS Glue execution times to handle massive data loads efficiently.
• Collaboration: Partner with cross-functional teams including data governance, risk, and analytics to deliver high-quality data assets for business reporting and machine learning initiatives.
• 
  

Qualifications & Technical Requirements

• Experience: Minimum 7 years of total hands-on experience in data engineering, data warehousing, or big data solutions, with demonstrated experience in the banking or financial services domain.
• Cloud Computing: Strong expertise in AWS ecosystem and cloud data architectures.
• Big Data & Processing: Advanced proficiency in PySpark for distributed data processing.
• ETL & Integration: Deep practical experience with AWS Glue for serverless data integration and ETL jobs.
• Orchestration Tools: Proven track record of managing workflow scheduling and dependency management using Apache Airflow.
• Database Skills: Strong SQL skills and experience working with relational and columnar databases (e.g., PostgreSQL, Snowflake, AWS Redshift).
• Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related quantitative field.
• 
  
• Skills: aws,data solutions,data