Build and maintain scalable AWS data platforms, including cloud-native data lakes, warehouses, and reliable batch or streaming pipelines. Develop SQL transformations and Python-based ETL using services such as S3, Glue, EMR, Redshift, and Athena. Monitor pipeline performance, reliability, and data quality; troubleshoot issues and apply security, scalability, and cost-efficiency best practices. Collaborate with architects, DevOps, QA, product teams, and business stakeholders while contributing to documentation, code reviews, and delivery standards.
Job Description:
Job Description – Data Engineer (AWS)1. Basic Information
- Job Title: Data Engineer (AWS)
- Experience: 3 to 7 Years
We are looking for a hands-on Data Engineer – AWS with 3 to 7 years of experience in developing, building, and maintaining 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 candidate should have strong hands-on expertise in AWS data services, SQL, and Python, along with experience in building reliable batch and streaming pipelines in a global delivery environment.
3. Must-Have SkillsCloud & Data Engineering (AWS)- Strong hands-on experience with:
- 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 processing and basic exposure to streaming concepts
Strong SQL skills (mandatory):
- 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 (preferred)
Good understanding of:
- Data modeling
- Transformations
- Performance tuning
- Hands-on experience with Spark / PySpark
- Experience handling:
- Structured and semi-structured data
- Knowledge of:
- Schema evolution
- Data quality checks
- Validation logic
- Working knowledge of Infrastructure as Code (Terraform / CloudFormation)
- Basic experience with CI/CD pipelines for data workloads
- Understanding of logging and monitoring using AWS CloudWatch
- Ability to work with architects, DevOps, QA, and business stakeholders
- Good communication skills to clearly explain technical concepts
- Experience with streaming technologies (Amazon Kinesis / Kafka)
- Familiarity with Lakehouse and modern data platform architectures
- Integration experience with BI / reporting tools
- Basic knowledge of:
- Data governance
- Data quality
- Metadata management
- Awareness of AWS cost optimization (FinOps basics)
- Experience in Agile delivery models with global teams
- Exposure to AI / ML use cases
- Design and build scalable ETL/ELT pipelines on AWS
- Develop:
- SQL-based data transformations
- Python-based data pipelines
- Implement data ingestion pipelines using S3, Glue, EMR
- Build data models optimized for analytics, performance, and cost efficiency
- Support deployment and execution of data pipelines
- Monitor:
- Pipeline performance
- Reliability
- Data quality
- Troubleshoot data issues and perform root cause analysis
- Apply best practices for:
- Security
- Reliability
- Scalability
- Work with architects and product teams to understand requirements
- Translate business needs into AWS data engineering solutions
- Contribute to:
- Documentation
- Code reviews
- Engineering best practices
- Bachelor’s or Master’s degree (or equivalent) in:
- Computer Science
- Information Technology
- Data Engineering
- or related field
- AWS Certified:
- Solutions Architect
- DevOps (Professional)
- Snowflake Core Certification (optional)
Location:
DGS India - Mumbai - Goregaon Prism TowerBrand:
MerkleTime Type:
Full timeContract Type:
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