AWS Data Engineer — Real-Time Pipelines & Lakehouse

Capgemini; New Jersey; Hybrid; USD 79,000 - 95,000; Full time

Benefits offered by this job

Paid time off

Health insurance

Retirement savings

Life and disability insurance

Employee assistance programs

Job summary

Capgemini is seeking an AWS Data Engineer to design and maintain scalable cloud-based data solutions in Whippany, NJ. You will build ETL/ELT pipelines, manage data lakes and warehouses on Redshift or Snowflake, and implement CI/CD with Terraform/CloudFormation.

The role requires hands-on AWS experience, Python/SQL, and strong collaboration with data scientists and business stakeholders to enable analytics and ML initiatives.

Qualifications

• Bachelor's degree in a related field is required.
• 5+ years of data engineering and cloud experience on AWS.
• Strong hands-on with Python, SQL, Spark, and ETL/ELT development.
• Experience with Redshift, Snowflake, data lakes, and real-time processing.

Responsibilities

• Design scalable data architectures using AWS services (S3, Redshift, Glue, Athena, EMR).
• Build and maintain ETL/ELT pipelines for structured and unstructured data.
• Develop real-time and batch ingestion with AWS-native tools and Kafka.
• Utilize Python, Spark, Scala, and AWS tools for processing and transformation.
• Collaborate with data scientists and stakeholders to deliver scalable data platforms.

Skills

AWS data services

Python

SQL

Spark

ETL/ELT

CI/CD

DevOps

Problem solving

Communication

Education

Bachelor's degree

Tools

S3

Redshift

Snowflake

Glue

Athena

EMR

DynamoDB

Lambda

Kinesis

Kafka

Airflow

Terraform

CloudFormation

Job description

Capgemini is seeking an AWS Data Engineer to design and maintain scalable cloud-based data solutions in Whippany, NJ. You will build ETL/ELT pipelines, manage data lakes and warehouses on Redshift or Snowflake, and implement CI/CD with Terraform/CloudFormation.

The role requires hands-on AWS experience, Python/SQL, and strong collaboration with data scientists and business stakeholders to enable analytics and ML initiatives.