Data Engineer

Job Descreption Looking for a hands‑on Senior Data Engineer – AWS with experience to development, build, and maintain scalable, secure, and high‑performance data platforms on AWS. This is an individual contributor role focused on data pipeline development, cloud data engineering, and analytics enablement. The role requires strong hands‑on skills in AWS data services, SQL, and Python, along with experience building reliable batch and streaming data pipelines in a global delivery environment. Must have skills

Experience designing cloud‑native data lakes and data warehouse architectures Solid understanding of batch data pipelines and basic exposure to streaming concepts SQL & Python (Mandatory) Strong SQL skills (mandatory) Writing complex queries, joins, aggregations, and transformations Experience working with large datasets in Redshift / Athena Strong Python skills (mandatory) Python for data engineering and ETL use cases Experience with PySpark / Spark is a strong plus

SQL & Python (Mandatory) Strong SQL skills (mandatory) Writing complex queries, joins, aggregations, and transformations Experience working with large datasets in Redshift / Athena Strong Python skills (mandatory) Python for data engineering and ETL use cases Experience with PySpark / Spark is a strong plus Good understanding of data modeling, transformations, and performance tuning

Key responsibiltes Data Engineering & Development Design and build scalable ETL / ELT pipelines on AWS Develop SQL‑based data transformations and Python‑based data pipelines Implement data ingestion pipelines using AWS services such as S3, Glue, EMR Build data models optimized for analytics, performance, and cost efficiency Platform & Operations Support deployment and execution of data pipelines across environments Monitor pipeline performance, reliability, and data quality Troubleshoot data pipeline issues and perform root‑cause analysis Apply best practices for security, reliability, and scalability

Must have skills Cloud & Data Engineering (AWS) Strong hands‑on experience with AWS data services, including: - Amazon S3 - AWS Glue - Amazon Athena - Amazon Redshift - Amazon EMR Experience designing cloud‑native data lakes and data warehouse architectures Solid understanding of batch data pipelines and basic exposure to streaming concepts

Good to have skills Exposure to streaming technologies such as Amazon Kinesis or Kafka Familiarity with Lakehouse and modern data platform patterns Experience integrating AWS data platforms with BI / reporting tools Basic knowledge of data governance, data quality, and metadata concepts Awareness of AWS cost optimization best practices Experience working in Agile delivery models, with global clients Exposure to AI / ML Key responsibiltes

Platform & Operations Support deployment and execution of data pipelines across environments Monitor pipeline performance, reliability, and data quality Troubleshoot data pipeline issues and perform root‑cause analysis Apply best practices for security, reliability, and scalability Collaboration & Delivery Work closely with architects and product teams to understand requirements Translate business and analytics needs into working AWS data solutions Contribute to documentation, code reviews, and engineering standards Education Qulification

Certification If Any 1.AWS Certified Solutions Architect / DevOps – Professional 2. Snowflake Core Shift timing

Good understanding of data modeling, transformations, and performance tuning Data Processing & Engineering Hands‑on experience with distributed data processing frameworks (Spark / PySpark) Experience handling structured and semi‑structured data Understanding of schema evolution, data quality checks, and validation logic DevOps & Platform Basics

DevOps & Platform Basics Working knowledge of Infrastructure as Code (Terraform and/or CloudFormation) Basic experience with CI/CD pipelines for data workloads Understanding of logging and monitoring using CloudWatch Collaboration