Job Summary:

EY is seeking an AWS Senior Data Engineer to design, develop, and maintain robust data pipelines and storage solutions within a cloud ecosystem. The role requires collaborating with cross-functional teams, ensuring data quality, and supporting business needs with scalable solutions.

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines using Python, Spark, and AWS services.
• Source and process data from on-prem Oracle databases, batch files, and Kafka streams.
• Build and manage ETL processes via AWS Glue and EMR for both batch and streaming datasets.
• Implement storage using the Medallion Architecture in AWS S3, Redshift, and Oracle.
• Use Airflow and other tools for orchestrating workflows and data optimizations.
• Apply CI/CD deployment processes using Terraform and associated tools.
• Monitor systems and workflow reliability using CloudWatch, Datadog, and Splunk.
• Ensure data integrity and communicate effectively with business stakeholders .

Key Technical Skills:

Python, Apache Spark, AWS S3, Kafka, Redshift, Oracle Database, AWS Glue, Apache Airflow, AWS EMR, Terraform, CloudWatch, Datadog, Splunk

Requirements:

• At least 5 years of relevant experience with a strong AWS data engineering background.
• Skilled in Python, Spark, and working with cloud technologies (especially AWS).
• Experience processing data with Kafka, and using Redshift and Oracle for data management.
• Hands-on ETL expertise with AWS Glue and EMR; workflow management with Airflow.
• Familiarity with automation and infrastructure-as-code using Terraform.
• Good to have: AWS DataSync, DMS, Athena, Lake Formation, SNS, SQS.
• Excellent communication skills for stakeholder collaboration.

Why Join EY:

EY provides a supportive, global culture committed to technology-driven transformation and building a better working world. Employees enjoy opportunities for personal growth, collaboration, and making an impact in large-scale, diverse projects for top-tier clients.