AWS Data Engineer

Job Title: AWS Data Engineer Location: Bangalore, India Work Mode: Hybrid About the Role We are seeking a highly skilled Data Engineer to design, build, and optimize scalable data pipelines and lakehouse solutions on AWS. The ideal candidate will have strong experience in enterprise-scale data engineering, ETL development, and modern cloud-based data platforms. This role will be responsible for building and maintaining Medallion Architecture (Bronze, Silver, Gold) data pipelines to support analytics, reporting, and business intelligence initiatives. Key Responsibilities Design, develop, and maintain AWS-based data pipelines following Medallion Architecture (Bronze, Silver, Gold). Build and optimize ETL/ELT workflows using AWS Glue, Spark, and Python. Develop and manage data ingestion pipelines using AWS DMS and Kinesis. Implement data transformation and enrichment processes to support analytics and reporting requirements. Integrate data solutions with AWS Athena, Redshift, and other downstream data consumers. Create and maintain datasets using Parquet and Apache Iceberg formats. Ensure data quality, consistency, governance, and lineage across the data platform. Implement monitoring, logging, and alerting mechanisms to maintain pipeline reliability. Collaborate with Data Architects, Data Analysts, and Business Stakeholders to understand data requirements and deliver scalable solutions. Participate in CI/CD implementation and automation for ETL deployment and maintenance. Support data cataloging, governance, and security initiatives using AWS Lake Formation and/or DataZone. Required Skills & QualificationsTechnical Skills Strong hands-on experience with: AWS Glue AWS DMS (Database Migration Service) AWS Kinesis Amazon Athena Amazon Redshift Apache Spark / PySpark Python SQL Experience working with: Parquet Apache Iceberg Data Lake and Lakehouse architectures Knowledge of Git version control and CI/CD pipelines for data engineering workflows. Experience with data governance, metadata management, and cataloging tools such as AWS Lake Formation and AWS DataZone. Experience 5+ years of experience in Data Engineering. Proven experience building enterprise-scale ETL/ELT and Lakehouse solutions. Experience handling large-scale data processing and analytics workloads. Strong understanding of data modeling, performance optimization, and data quality practices. Preferred Qualifications AWS Certified Data Analytics – Specialty. AWS Certified Solutions Architect. Experience with modern data platform modernization initiatives. Exposure to Agile/Scrum development methodologies. Key Performance Indicators (KPIs) Data pipeline reliability and uptime. Data freshness and timely availability. Adherence to ETL job SLAs. Data quality and accuracy metrics. Successful delivery of Gold-layer datasets for MVP and business consumption. Operational efficiency and automation of data workflows.