Maintains, cleans, manipulates, and secures operational and analytics data. Designs scalable data pipeline architectures, builds complex data sets and analytics tools, automates manual processes, improves infrastructure, troubleshoots database issues, and supports data, product, design, analytics, and data science teams with technical data needs.
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.
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)
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 Have
Key Responsibilities
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 Responsibilities
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
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 OrchidBrand:
MerkleTime Type:
Full timeContract Type:
PermanentSimilar Jobs
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