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.