Data Engineer

Key Skills: AWS Glue, Amazon S3, Amazon Redshift, AWS Lambda, PySpark, Python, Snowflake Mode of Work: Work From Office (5 Days) Job Location: Hyderabad / Pune / Coimbatore Experience: 4 to 12 Years About the job: As an AWS Data Engineer, you will be responsible for designing, developing, and maintaining scalable cloud-native data engineering solutions on the AWS platform. You will build high-performance data pipelines, implement ETL/ELT processes, and develop modern data platforms that support enterprise analytics, reporting, and business intelligence initiatives. Working closely with architects, business stakeholders, and cross-functional teams, you will leverage AWS services such as Glue, S3, Redshift, Lambda, along with Snowflake, PySpark, and Python, to build secure, scalable, and reliable data solutions for global insurance clients. The ideal candidate should possess strong expertise in AWS data engineering services, cloud-native architectures, data warehousing, and modern data integration practices. Know your team: At ValueMomentum Technology Solution Centers, we are a team of passionate engineers solving complex business challenges across the P&C insurance value chain. Our expertise spans Cloud Engineering, Application Engineering, Data Engineering, Core Engineering, Quality Engineering, and Domain capabilities. Through our Infinity Program, we invest in continuous learning, role-based skill development, and opportunities to work on impactful global transformation programs. Responsibilities: • Design, develop, and maintain scalable data pipelines using AWS services. • Build and optimize ETL/ELT workflows using AWS Glue and Snowflake. • Develop cloud-native data solutions utilizing AWS Glue, S3, Redshift, Lambda, and PySpark. • Design and maintain enterprise data lakes and cloud data warehouses. • Collaborate with business analysts, architects, and stakeholders to translate business requirements into technical solutions. • Develop scalable data processing frameworks using Python and PySpark. • Optimize data pipeline performance through partitioning, indexing, caching, and query tuning. • Implement metadata management, data lineage, governance, and security best practices. • Ensure data quality through validation, reconciliation, and monitoring frameworks. • Monitor, troubleshoot, and optimize production data pipelines for reliability and availability. • Document data architecture, workflows, and operational procedures. Requirements: • 4–9 years of experience in AWS Data Engineering and cloud-based data platforms. • Strong hands-on experience with AWS Glue, Amazon S3, Amazon Redshift, and AWS Lambda. • Experience building scalable ETL/ELT pipelines and cloud-native data solutions. • Strong expertise in Snowflake data warehousing and performance optimization. • Proficiency in Python, PySpark, and SQL. • Experience with workflow orchestration tools such as Apache Airflow or AWS Step Functions. • Strong understanding of Data Warehousing concepts, dimensional modeling, and distributed computing. • Experience working with structured and semi-structured data formats such as CSV, JSON, and Parquet. • Knowledge of metadata management, data governance, data lineage, and security best practices. • Strong analytical, debugging, troubleshooting, and performance tuning skills. • Excellent communication skills with experience working directly with business stakeholders and clients. • Experience working in Agile/Scrum development environments. Good To Have: • Experience with real-time streaming technologies such as Kafka or Amazon Kinesis. • Exposure to Azure Data Factory or GCP Dataflow. • Experience with Infrastructure as Code (Terraform/CloudFormation). • AWS Certified Data Engineer – Associate or other AWS certifications. • Experience in the Property & Casualty (P&C) Insurance domain.