Data Engineer

Job Description:
Job Description – Data Engineer (AWS)1. Basic Information
Job Title: Data Engineer (AWS)
Experience: 3 to 7 Years
2. Role Overview
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
SQL & Python (Mandatory)
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
Data Processing & Engineering
Hands-on experience with Spark / PySpark
Experience handling:
Structured and semi-structured data
Knowledge of:
Schema evolution
Data quality checks
Validation logic
DevOps & Platform Basics
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
Collaboration
Ability to work with architects, DevOps, QA, and business stakeholders
Good communication skills to clearly explain technical concepts
4. Good-to-Have Skills
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
5. Key ResponsibilitiesData Engineering & Development
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
Platform & Operations
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
Collaboration & Delivery
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
6. Education Qualification
Bachelor’s or Master’s degree (or equivalent) in:
Computer Science
Information Technology
Data Engineering
or related field
7. Certifications (Preferred)
AWS Certified:
Solutions Architect
DevOps (Professional)
Snowflake Core Certification (optional)
Location:
DGS India - Mumbai - Goregaon Prism Tower
Brand:
Merkle
Time Type:
Full time
Contract Type:
Permanent