Data Engineer
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.
Key Skills for This Role
Full Job Posting
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.
Cloud & Data Engineering (AWS)
Strong hands-on experience with: Amazon S3 AWS Glue Amazon Athena Amazon Redshift Amazon EMR
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
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)
Python for data engineering and ETL use cases
Experience with PySpark / Spark (preferred)
Good understanding of: Data modeling Transformations Performance tuning
Data modeling
Transformations
Performance tuning
Data Processing & Engineering
Hands-on experience with Spark / PySpark
Experience handling: Structured and semi-structured data
Structured and semi-structured data
Knowledge of: Schema evolution Data quality checks Validation logic
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
- 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
Data Engineering & Development
Design and build scalable ETL/ELT pipelines on AWS
Develop: SQL-based data transformations Python-based data pipelines
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
Pipeline performance
Reliability
Data quality
Troubleshoot data issues and perform root cause analysis
Apply best practices for: Security Reliability Scalability
Security
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
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
- Computer Science
- Information Technology
- Data Engineering
- or related field
7. Certifications (Preferred)
AWS Certified: Solutions Architect DevOps (Professional)
Solutions Architect
DevOps (Professional)
Snowflake Core Certification (optional)
About Merkle
Merkle is a dentsu-owned global experience consultancy delivering data, technology, marketing, and customer-experience services to brands.
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