The Value Maximizer
Responsibilities
Design, develop, and maintain ETL pipelines using AWS Glue, Glue Studio, and Glue Catalog
Ingest, transform, and load large datasets from structured and unstructured sources into AWS data lakes/warehouses
Work with S3, Redshift, Athena, Lambda, and Step Functions for data storage, query, and orchestration
Build and optimize PySpark/Scala scripts within AWS Glue for complex transformations
Implement data quality checks, lineage, and monitoring across pipelines
Collaborate with business analysts, data scientists, and product teams to deliver reliable data solutions
Ensure compliance with data security, governance, and regulatory requirements (BFSI preferred)
Troubleshoot production issues and optimize pipeline performance.
Required Qualifications
9+ years of experience in Data Engineering, with at least 5+ years on AWS cloud data services
Strong expertise in AWS Glue, S3, Redshift, Athena, Lambda, Step Functions, CloudWatch
Proficiency in PySpark, Python, SQL for ETL and data transformations
Experience in data modeling (star, snowflake, dimensional models) and performance tuning
Hands‑on experience with data lake/data warehouse architecture and implementation
Strong problem‑solving skills and ability to work in Agile/Scrum environments.
Preferred Qualifications
AWS Certified Data Analytics - Specialty or AWS Solutions Architect certification
Familiarity with CI/CD pipelines for data engineering (CodePipeline, Jenkins, GitHub Actions)
Knowledge of BI/Visualization tools like Tableau, Power BI, QuickSight
Seniority level:
Mid-Senior level
Employment type:
Full‑time
Job function:
Other
Industries:
IT Services and IT Consulting
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Design, develop, and maintain ETL pipelines using AWS Glue, Glue Studio, and Glue Catalog
Ingest, transform, and load large datasets from structured and unstructured sources into AWS data lakes/warehouses
Work with S3, Redshift, Athena, Lambda, and Step Functions for data storage, query, and orchestration
Build and optimize PySpark/Scala scripts within AWS Glue for complex transformations
Implement data quality checks, lineage, and monitoring across pipelines
Collaborate with business analysts, data scientists, and product teams to deliver reliable data solutions
Ensure compliance with data security, governance, and regulatory requirements (BFSI preferred)
Troubleshoot production issues and optimize pipeline performance.
Required Qualifications
9+ years of experience in Data Engineering, with at least 5+ years on AWS cloud data services
Strong expertise in AWS Glue, S3, Redshift, Athena, Lambda, Step Functions, CloudWatch
Proficiency in PySpark, Python, SQL for ETL and data transformations
Experience in data modeling (star, snowflake, dimensional models) and performance tuning
Hands‑on experience with data lake/data warehouse architecture and implementation
Strong problem‑solving skills and ability to work in Agile/Scrum environments.
Preferred Qualifications
AWS Certified Data Analytics - Specialty or AWS Solutions Architect certification
Familiarity with CI/CD pipelines for data engineering (CodePipeline, Jenkins, GitHub Actions)
Knowledge of BI/Visualization tools like Tableau, Power BI, QuickSight
Seniority level:
Mid-Senior level
Employment type:
Full‑time
Job function:
Other
Industries:
IT Services and IT Consulting
#J-18808-Ljbffr