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Employment: Full-time · Permanent — employment source
Time Type: Full time Contract Type:
Contract Type: Permanent
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People management: Individual contributor stated — people management source
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:
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Tools in this posting

Python
SQL
AWS
Redshift
S3
Spark
Terraform
PySpark
Kafka

Source

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:

Solid understanding of batch data pipelines and basic exposure to streaming concepts SQL & Python (Mandatory) Strong SQL skills (mandatory)

Experience working with large datasets in Redshift / Athena Strong Python skills (mandatory) Python for data engineering and ETL use cases

Strong Python skills (mandatory) Python for data engineering and ETL use cases Experience with PySpark / Spark is a strong plus

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

SQL & Python (Mandatory) Strong SQL skills (mandatory) Writing complex queries, joins, aggregations, and transformations

Min 3 and max upto 5. Must Have 'Cloud & Data Engineering (AWS) Strong hands‑on experience with AWS data services, including:

Must Have 'Cloud & Data Engineering (AWS) Strong hands‑on experience with AWS data services, including: - Amazon S3

- Amazon S3 - AWS Glue - Amazon Athena

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

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

Key ResponsibilitiesData Engineering & Development Design and build scalable ETL / ELT pipelines on AWS Develop SQL‑based data transformations and Python‑based data pipelines

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

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

- Amazon Athena - Amazon Redshift - Amazon EMR

Writing complex queries, joins, aggregations, and transformations Experience working with large datasets in Redshift / Athena Strong Python skills (mandatory)

Strong hands‑on experience with AWS data services, including: - Amazon S3 - AWS Glue

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

DevOps & Platform Basics Working knowledge of Infrastructure as Code (Terraform and/or CloudFormation) Basic experience with CI/CD pipelines for data workloads

Good communication skills to explain technical concepts clearly Good to HaveExposure to streaming technologies such as Amazon Kinesis or Kafka Familiarity with Lakehouse and modern data platform patterns

Job description

View original posting ↗

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

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Location & working pattern

DGS India - Pune - Indiqube Orchid

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Open source at dentsuaegis.wd3.myworkdayjobs.com ↗Read the full description ↓

Status in our records: Active
First seen by us: Sep 2, 2026
Recorded sightings: 35
Last seen by us: Oct 9, 2026

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