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 Skills

Cloud & 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 Responsibilities

Data 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