Data Engineer

Dentsu Aegis Network Ltd.; Mumbai; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Dentsu Aegis Network Ltd. in Mumbai is seeking a hands-on Data Engineer – AWS with 3 to 7 years of experience to build scalable data pipelines and analytics platforms on AWS.

The role focuses on ETL/ELT development, batch and streaming pipelines, SQL and Python, and collaboration with architects, DevOps, QA, and business stakeholders.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, IT, or Data Engineering.
• 3 to 7 years of hands-on experience as a data engineer on AWS.
• Strong SQL and Python for data engineering and ETL use cases.

Responsibilities

• Design and build scalable ETL/ELT pipelines on AWS.
• Develop SQL-based data transformations and Python-based data pipelines.
• Implement data ingestion pipelines using S3, Glue, and EMR.
• Build data models optimized for analytics, performance, and cost efficiency.
• Collaborate with architects, DevOps, QA, and business stakeholders.

Skills

AWS

Amazon S3

AWS Glue

Amazon Athena

Amazon Redshift

Amazon EMR

SQL

Python

PySpark

Spark

Education

Bachelor’s or Master’s degree in Computer Science / Information Technology / Data Engineering

Tools

Terraform

CloudFormation

CI/CD (data workloads)

Job description

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

About dentsu

For over 120 years, innovation has been a core tenet of our offering – exploring new ways to reach, engage and nurture relationships with audiences. Together we drive a multiplier effect for clients at a global scale, through the development of Integrated Growth Solutions that are underpinned by our promise to clients: innovating to impact. Be a force for good. Sustainability is a vital part of our business and an important area of focus for our clients. We’re leading the way – helping to build a more sustainable planet. Dream loud. In this moment of transformation, we need our people to be fearless, embracing change and ambiguity, driven by the love for their work and excitement for the future. Team without limits. We create opportunities for connection and collaboration between our colleagues and clients, building a sense of belonging and having some fun along the way.

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