Description

You will design, build, and optimize scalable data pipelines and infrastructure using Python and AWS services.

This role is hybrid.

Responsibilities

• Build and optimize ETL/ELT processes to ingest data from various sources into data lakes and warehouses.
• Design scalable data architectures by translating business requirements into technical implementations.
• Monitor and troubleshoot data workflows to improve performance and cost-efficiency.
• Implement data validation and governance practices to ensure quality and consistency.
• Automate engineering tasks using Python and deploy workflows via CI/CD pipelines.

Required Skills

• 9+ years of professional experience in data engineering.
• Strong proficiency in Python, including pandas, pyspark, or boto3.
• Hands-on experience with AWS Glue, S3, Redshift, Athena, Lambda, and Kinesis.
• Deep knowledge of SQL and relational databases like PostgreSQL and MySQL.
• Experience managing NoSQL databases, specifically DynamoDB.
• Proficiency with Git-based workflows and version control.
• Experience implementing security via IAM roles.
• Familiarity with CI/CD tools including Jenkins, Git, and CodePipeline.
• Strong analytical and debugging skills for complex data environments.

Preferred Skills

• Knowledge of big data frameworks such as Hadoop and Spark.

Key Skills

Education

Any Gradute