Role: AWS Data Engineer Preferred Skills · ETL Development · AWS ETL Services · Data Pipeline Development · SQL & Python Programming · Data Warehousing Experience: 4-8 years Detailed Responsibilities & Skills Must Have Technical Skills Strong hands-on experience with: · AWS ETL Services (Glue, Lambda, Step Functions) · AWS AppFlow (mainly ODATA connector) · AWS Database Migration Service (DMS) · OData Services and API Integration · Amazon Redshift · Data Lake & Amazon S3 · Python, PySpark, SQL, Data Modeling · Data Modeling & ETL Development · Data Quality & Performance Optimization · Enterprise Data Integration (SAP, Oracle, Azure, Oracle Cloud) Key Responsibilities · Design, develop, and maintain scalable ETL pipelines using AWS Glue, Lambda, Step Functions, and Redshift. · Build and orchestrate AWS workflows using Master, Parent, and Child Step Functions to execute Glue jobs and Redshift stored procedures. · Develop optimized Glue jobs using Python and PySpark, ensuring efficient DPU utilization and performance tuning. · Design and implement data ingestion pipelines from multiple enterprise data sources including SAP, Oracle Database, Oracle Cloud, Azure, and other heterogeneous systems. · Capture change data / delta data to ingestion layer. · Store, transform, and manage data in Amazon S3 (Object Store, Iceberg Tables) and Amazon Redshift. · Perform data cleansing, transformation, and validation to ensure high data quality and reliability. · Identify opportunities to improve ETL performance, data quality, and overall pipeline efficiency. · Design and implement data models,



including fact and dimension tables, to support reporting and analytics. · Transform unstructured and semi-structured data into structured datasets suitable for downstream consumption. · Develop BI-ready datasets and understand KPI calculations across multiple business domains. · Collaborate with business and analytics teams to understand reporting requirements and translate them into technical solutions. · Troubleshoot production issues and support enhancements for existing data pipelines. Additional Responsibilities · Design and develop data ingestion pipelines using AWS AppFlow, AWS DMS, OData services, APIs, and batch ingestion frameworks. · Integrate enterprise applications such as SAP (OData/CDS Services), Oracle Database, Oracle Cloud, Azure, and other heterogeneous sources with AWS data platforms. · Configure and optimize AWS DMS for full load and Change Data Capture (CDC) migration scenarios. · Build and maintain AWS AppFlow integrations for secure and automated data movement between SaaS applications and AWS services. · Troubleshoot and optimize data ingestion performance across AppFlow, DMS, Glue, and Step Functions.



Required Experience · 4–8 years of experience in Data Engineering with strong AWS expertise. · Proven experience building enterprise-scale ETL pipelines on AWS. · Strong proficiency in Python, PySpark, and SQL. · Experience working with Amazon Redshift as a developer, including stored procedures and performance optimization. · Hands-on experience with AWS Step Functions for workflow orchestration. · Experience in AWS Glue job development, optimization, and Glue Crawlers. · Experience handling large datasets and optimizing ETL workloads. · Valuable understanding of data modeling concepts, including star schema, fact tables, and dimension tables. · Familiarity with cloud-based data lakes and modern data architectures. · Experience integrating data from enterprise applications such as SAP, Oracle, Azure, and Oracle Cloud. · Understanding of KPI calculations and preparation of analytics-ready datasets. Good to Have · Knowledge of Apache Iceberg on Amazon S3. · Experience with CI/CD for AWS data pipelines. · Exposure to Infrastructure as Code (CloudFormation/Terraform). · Understanding of data governance, data cataloging, and security best practices. · Experience with monitoring and troubleshooting AWS data services. Soft Skills · Strong analytical and problem-solving skills. · Good communication and stakeholder management. · Ability to work independently as well as within cross-functional teams. · Strong documentation and knowledge-sharing practices. · Ability to work in an Agile development environment.

📌 Data Engineer (1744) (Mumbai)
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📍 Mumbai