AWS Data Engineer
Summary
Build and maintain scalable AWS data pipelines using PySpark, Glue, Step Functions, and Lambda for a fintech client, while automating infra with Terraform and CI/CD.
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.