Data Engineer

Dentsu Global Services; Pune District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Merkle is seeking a hands-on Senior Data Engineer - AWS for its DGS India Pune delivery team. This individual contributor role focuses on developing, building and maintaining scalable data platforms on AWS for batch and streaming data pipelines and analytics enablement.

You will collaborate with architects, DevOps, QA and business stakeholders to design secure data solutions, optimize performance, and implement data quality checks across global delivery projects.

Qualifications

• Strong SQL skills with complex queries, joins and aggregations
• Proficient Python for ETL and data engineering tasks
• Hands-on experience with AWS data services (S3, Glue, Athena, Redshift, EMR)
• Experience building batch and streaming data pipelines
• Familiarity with distributed processing frameworks (Spark/PySpark)
• Understanding data modeling, quality checks and schema evolution

Responsibilities

• Design and build scalable ETL/ELT pipelines on AWS
• Develop SQL-based data transformations and Python-based pipelines
• Implement data ingestion using S3, Glue, EMR
• Create data models optimized for analytics, performance and cost
• Collaborate with architects, DevOps, QA and stakeholders
• Ensure data quality, security, reliability and monitoring

Skills

SQL

Python

AWS data services

Redshift

Athena

S3

PySpark

Spark

Tools

Spark

PySpark

Terraform

CloudFormation

Job description

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.

Job Description

Min 3 and max upto 5.

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

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 Orchid

Brand

Merkle

Time Type

Full time

Contract Type

Permanent#DGS