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Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable data pipelines and architectures using PySpark and AWS technologies.
Develop ETL/ELT solutions, data warehouses, and data lakes supporting large-scale enterprise data processing and analytics.
Collaborate with stakeholders and engineering teams to deliver secure, reliable, and performant data products and support machine learning model deployment.
Minimum Requirements
Strong programming skills in Python and PySpark, with hands-on experience in developing and optimizing AWS Glue ETL jobs.
Experience with AWS Cloud services including S3, Glue, EMR, Lambda, EC2, DynamoDB, IAM, CloudWatch, and CloudTrail.
Proven experience working with data engineering practices, scalable data pipeline development, and modern data platform architectures (Data Lake, Lakehouse).
Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced with Databricks and/or Snowflake platforms and modern data engineering frameworks.
Skilled at implementing data quality controls, validation frameworks, and automated testing in code delivery.
Able to lead or guide complex assignments, advise decision-making, and collaborate cross-functionally with architects, product owners, and business stakeholders.