About this role
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. Job Description: Min 3 and max upto 5.
Must Have '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
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
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
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
Ability to work closely with architects, DevOps, QA, and business stakeholders Good communication skills to explain technical concepts clearly
Good to HaveExposure 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 ResponsibilitiesData 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
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
Location: DGS India - Pune - Indiqube Orchid Brand: Merkle Time Type: Full time Contract Type: Permanent