Description

Role Overview:

Senior Data Engineer responsible for building and maintaining event-driven data pipelines using AWS services and Python.

Key Responsibilities:

• 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 Skills:

• 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)

Key Skills

Education

Any Graduate