18 days ago
Base Salary
$81k - $91k/yr
Responsibilities
- Design and implement scalable data architectures using AWS services including S3, Redshift, Glue, Athena, EMR, DynamoDB, Lambda, and Kinesis.
- Build, optimize, and maintain ETL/ELT pipelines for structured and unstructured data, including batch and real-time ingestion with Kafka.
- Develop data transformation workflows using Spark, Python, and Scala.
- Build and maintain data lakes on Amazon S3 and data warehouses on Redshift and Snowflake.
- Design and optimize data models, database schemas, partitioning strategies, and query performance.
- Implement data quality checks, validation rules, automated error handling, metadata management, data cataloging, and lineage.
- Implement CI/CD pipelines and automate infrastructure provisioning using Terraform, CloudFormation, and infrastructure-as-code practices.
- Monitor and support data pipelines and cloud infrastructure using CloudWatch, CloudTrail, and AWS Config.
- Apply AWS security practices involving IAM, KMS encryption, VPC networking, and Secrets Manager.
- Troubleshoot pipeline failures, performance bottlenecks, and data quality issues while providing technical guidance on AWS data architecture.
Requirements
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
- At least five years of experience in data engineering and cloud-based data platforms.
- Strong hands-on experience with AWS data services including S3, Redshift, Glue, Athena, EMR, and DynamoDB.
- Proficiency in Python, SQL, Spark, and ETL/ELT development.
- Experience building and managing data lakes, data warehouses, and modern data platforms.
- Knowledge of Kafka, real-time data processing, and distributed data architectures.
- Experience with Terraform, CloudFormation, CI/CD pipelines, and DevOps practices.
- Strong understanding of data modeling, database optimization, and performance tuning.
- Excellent analytical, problem-solving, and communication skills.
- Preferred qualifications include AWS Certified Data Engineer, AWS Certified Solutions Architect, or related AWS certifications; Snowflake and lakehouse architecture experience; familiarity with Airflow, Databricks, and modern data engineering frameworks; and experience supporting analytics, BI, and machine learning workloads.
Benefits
- Onsite/hybrid work in Whippany, NJ from day one.
- Paid time off including 12–25 vacation days depending on employee grade, company holidays, personal days, and sick leave.
- Medical, dental, and vision coverage, or provincial healthcare coordination in Canada.
- Retirement savings plans such as 401(k) in the U.S. or RRSP in Canada.
- Life and disability insurance.
- Employee assistance programs and other benefits based on local policy and eligibility.
- Regular full-time employees receive a comprehensive, non-negotiable benefits package.
Tech Stack
Amazon DynamoDBAmazon RedshiftApache AirflowApache KafkaApache SparkAWSDatabricksGitHub ActionsJenkinsPythonScalaSnowflakeSQLTerraform
Categories
Data Engineering
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.