Senior Data Engineer - AWS & Python

About the Role

Build and maintain event-driven data pipelines using AWS services such as Kinesis, MSK/Kafka, Lambda, Step Functions, SQS/SNS, and Glue/EMR.

Develop ETL/ELT workflows using Python and PySpark, ensuring performance, scalability, and cost efficiency.

Implement and optimize Spark-based data transformations, partitioning strategies, and data processing frameworks.

Design and manage data lake and warehouse structures using S3, Glue Catalog, Athena, and/or Redshift.

Build streaming solutions with checkpointing, stateful transformations, idempotency, and schema evolution.

Ensure high standards of data quality, observability, monitoring, and alerting (CloudWatch, Datadog, etc.).

Implement data security best practices including IAM, encryption (KMS), networking, and governance.

Create reusable frameworks, internal libraries, and CI/CD pipelines for automated deployments.

Collaborate with data scientists, analysts, and business teams to deliver well-modeled, reliable datasets.

Lead design reviews, mentor junior engineers, and contribute to engineering best practices.

Required Qualifications

• Overall 8+ yrs of experience
• 5+ years of professional experience in Data Engineering.
• Experience of working on Java is an advantage
• Strong expertise in Python and PySpark for large-scale data processing.
• Advanced hands-on experience with AWS (S3, Glue, EMR, Lambda, Step Functions, Kinesis/MSK, DynamoDB, Athena, Redshift).
• Deep experience building event-driven and streaming data pipelines.
• Strong SQL experience for analytical and ETL workloads.
• Hands-on experience with workflow orchestration tools such as Airflow or Step Functions.
• Experience with CI/CD, Git, and Infrastructure-as-Code (Terraform or CloudFormation).
• Strong understanding of distributed systems, Spark performance tuning, data modeling, and cloud cost optimization.
• Knowledge of data security, encryption, networking, and compliance best practices in cloud environments