Senior Data Engineer (AWS)

This role requires strong data Engineering& analysis expertise across SQL, Data modelling, DBT, Airflow, and Lakehouse/data lake architecture, with hands-on experience querying large-scale distributed datasets directly from data lakes.

Banking/financial services experience, exposure to ledger/ transactional/ regulatory data, support for migration/modernization program, familiarity, with data governance / metadata. regulatory controls.

Key Responsibilities

. Design, develop, and maintain scalable data pipelines using PySpark on AWS.

. Build and orchestrate ETL/ELT workflows using AWS Glue and AWS Step Functions.

. Develop serverless applications and automation using AWS Lambda.

. Write clean, efficient, and maintainable Python/PySpark code following engineering best practices.

. Provision and manage cloud infrastructure using Terraform (Infrastructure as Code).

. Implement and maintain CI/CD pipelines to automate code deployment, testing, and infrastructure changes.

. Monitor, troubleshoot, and optimize data pipelines for performance, reliability, and cost efficiency.

. Collaborate with business stakeholders to deliver data solutions.

. Follow DevOps, security, and coding standards throughout the engagement.

Required Skills

. Strong hands-on experience with PySpark and Python for data engineering.

. Experience developing ETL pipelines using AWS Glue.

. Proficiency with AWS Step Functions for workflow orchestration.

. Experience building serverless solutions using AWS Lambda.

. Hands-on experience with Terraform for Infrastructure as Code (IaC).

. Experience implementing CI/CD pipelines using tools such as GitLab, GitHub Actions, Jenkins, or similar.

. Good understanding of AWS services, data lakes, IAM, S3, CloudWatch, and monitoring.