Data Engineer

Posted 5 Hours Ago
Be an Early Applicant
3 Locations
In-Office
Senior level
AdTech • Marketing Tech
The Role
Build and maintain scalable AWS data platforms and batch or streaming pipelines. Develop SQL transformations and Python ETL workflows using services such as S3, Glue, EMR, Athena, and Redshift. Design data lakes, warehouses, and analytics-optimized models while monitoring performance, reliability, security, and data quality. Troubleshoot pipeline issues, support deployments, and collaborate with architects, DevOps, QA, product teams, and business stakeholders.
Summary Generated by Built In

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Job Description


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Project Details

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Business Title

Data Engineer

Years of Experience

Min 3 and max upto 7.

Job Descreption

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.

Must have skills

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 skills

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 responsibiltes

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

Education Qulification

1. Bachelor’s or Master Degree or equivalent Degree

Certification If Any

1.AWS Certified Solutions Architect / DevOps – Professional
2. Snowflake Core 

Shift timing

12 PM to 9 PM and / or  2 PM to 11 PM - IST time zone


Location:

DGS India - Pune - Indiqube Orchid

Brand:

Merkle

Time Type:

Full time

Contract Type:

Permanent

Skills Required

  • 3 to 7 years of relevant experience
  • Bachelor's or master's degree, or equivalent
  • Strong hands-on experience with AWS data services, including Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift, and Amazon EMR
  • Experience designing cloud-native data lakes and data warehouse architectures
  • Strong SQL skills, including complex queries, joins, aggregations, and transformations
  • Strong Python skills for data engineering and ETL
  • Experience building batch data pipelines and basic exposure to streaming concepts
  • Experience with distributed data processing frameworks such as Spark or PySpark
  • Understanding of data modeling, transformations, performance tuning, schema evolution, data quality checks, and validation logic
  • Working knowledge of Terraform and/or CloudFormation
  • Basic experience with CI/CD pipelines for data workloads
  • Understanding of logging and monitoring using Amazon CloudWatch
  • AWS Certified Solutions Architect or DevOps Professional certification
  • Snowflake Core certification
  • Exposure to Amazon Kinesis or Kafka
  • Familiarity with lakehouse and modern data platform patterns
  • Experience integrating AWS data platforms with BI or reporting tools
  • Knowledge of data governance, data quality, metadata, and AWS cost optimization
  • Experience working in Agile delivery models with global clients
  • Exposure to AI or machine learning

dentsu Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about dentsu and has not been reviewed or approved by dentsu.

  • Parental & Family Support — Paid parental leave at full pay and caregiver supports (including backup care) are emphasized as standout elements. Feedback suggests family-oriented benefits are a strong part of the package.
  • Leave & Time Off Breadth — Flexible or unlimited PTO, extensive paid holidays, and a year-end office closure are established components. Feedback suggests time-off policies are generous and add meaningful flexibility.
  • Retirement Support — A large, established 401(k) plan with employer matching is clearly documented. Feedback suggests retirement benefits feel competitive and straightforward.

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The Company
HQ: Minato
15,492 Employees

What We Do

We are dentsu. We team together to help brands predict and plan for disruptive future opportunities and create new paths to growth in the sustainable economy. We know people better than anyone else and we use those insights to connect brand, content, commerce and experience, underpinned by modern creativity. We are the network designed for what’s next

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