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# IT Consultant - Cloud Specialist L2

- **Company:** Capgemini
- **Location:** Hanover, NJ, United States
- **Experience:** Expert
- **Salary:** $90,688 - $141,710
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Amazon S3, Apache HTTP Server, Automation of Tests, Cloud Computing, Cloud Engineering, Code Review, Computer Programming, Continuous Integration, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Security, Data Systems, DevOps, Github, Identity and Access Management, Python (Programming Language), Enterprise Messaging Systems, Meta-Data Management, Cloud Services, Standard Sql, Shell Script, Transaction Data, Workflow Management Systems, Enterprise Data Management, AWS Cdk, Data Processing, Data Ingestion, Delivery Pipeline, AWS Lambda, Cloudformation, Event Driven Architecture, Data Lakes, Pyspark, Deployment Automation, AWS Lake Formation, AWS Glue, AWS Data Analytics, Data Management, Cloudwatch, Amazon Simple Queue Service (SQS), Terraform, Stream Processing, Data Pipelines, Serverless Computing, Jenkins, Amazon Redshift, Microservices
- **Published:** October 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e1c5b1d05ad98c56

## About the Role

We are seeking an experienced AWS Engineering Lead to design, develop, and lead enterprise-scale cloud-native data platforms on AWS. The ideal candidate will possess deep expertise in AWS data engineering services, modern data lake architectures, Apache Iceberg, and cloud-native development practices. This individual will provide technical leadership to engineering teams, define architecture standards, and deliver scalable, secure, and high-performing data solutions that support enterprise data and analytics initiatives., * Extensive experience designing and delivering enterprise-scale AWS data platforms.

 * Demonstrated experience leading engineering teams and technical initiatives.
 * Strong understanding of modern cloud-native architecture principles and best practices.
 * Experience designing and implementing large-scale enterprise data lakes.
 * Expertise in data integration, transformation, and orchestration patterns.
 * Strong problem-solving, communication, and stakeholder management skills.
 * Experience working in Agile delivery environments.

## Description

* Lead the architecture, design, and implementation of scalable AWS-based data platforms.
 * Provide technical leadership, guidance, and mentorship to data engineering teams.
 * Establish engineering standards, coding best practices, and CI/CD development methodologies.
 * Conduct architecture reviews, code reviews, and technical governance activities.
 * Drive continuous improvement in engineering quality, operational excellence, and platform reliability.
 * Support technical decision-making and ensure alignment with enterprise architecture standards., * Design, develop, and maintain cloud-native data ingestion, transformation, and orchestration solutions on AWS.
 * Build and support ETL/ELT pipelines using AWS Glue.
 * Develop serverless applications and data processing solutions using AWS Lambda.
 * Design and implement workflow orchestration using AWS Step Functions.
 * Build and manage enterprise data lake solutions utilizing Apache Iceberg on Amazon S3.
 * Enable efficient data access patterns through services such as Amazon Athena and Amazon Redshift., * Architect highly available, secure, scalable, and cost-efficient cloud solutions.
 * Design event-driven and microservices-based architectures.
 * Develop integration patterns leveraging APIs, messaging platforms, and AWS-native services.
 * Optimize platform performance, resilience, and operational efficiency.
 * Ensure solutions follow AWS security, governance, and compliance best practices., * Implement metadata management, schema evolution, partitioning strategies, and data lifecycle controls.
 * Leverage Apache Iceberg capabilities including time travel, compaction, and transactional data management.
 * Support large-scale batch and near real-time data processing requirements.
 * Ensure data quality, governance, observability, and security controls are implemented across the platform.
 * Partner with stakeholders to evolve data platform capabilities and roadmap initiatives., * Implement Infrastructure as Code using Terraform, CloudFormation, or AWS CDK.
 * Develop and maintain automated CI/CD pipelines utilizing GitHub Actions, Jenkins, or AWS CodePipeline.
 * Automate deployment, testing, monitoring, and operational processes.
 * Implement observability and monitoring solutions using AWS CloudWatch and operational dashboards.
 * Support production operations, incident resolution, and platform reliability initiatives., * Collaborate closely with Product Owners, Architects, Business Analysts, and Technology teams.
 * Translate business requirements into scalable technical solutions.
 * Manage project dependencies, technical risks, and delivery timelines.
 * Communicate architectural decisions, technical recommendations, and project status to stakeholders.
 * Drive successful project execution while maintaining high engineering standards., * AWS Glue
 * AWS Lambda
 * AWS Step Functions
 * Amazon S3
 * Amazon Athena
 * Amazon Redshift
 * AWS IAM
 * AWS CloudWatch
 * Amazon EventBridge
 * Amazon SNS
 * Amazon SQS
 * AWS Secrets Manager
 * AWS Lake Formation

Data Engineering

 * Enterprise Data Lake Architecture
 * Apache Iceberg
 * ETL/ELT Frameworks
 * Data Ingestion and Transformation
 * Schema Evolution
 * Data Partitioning Strategies
 * Data Compaction
 * Time Travel Concepts
 * Metadata Management

Programming

 * Python
 * PySpark
 * SQL
 * Shell Scripting

Cloud Architecture

 * Serverless Architecture
 * Event-Driven Design
 * Microservices Architecture
 * API Integration
 * Data Lake Architecture

DevOps

 * Terraform
 * AWS CloudFormation
 * AWS CDK
 * GitHub Actions
 * Jenkins
 * AWS CodePipeline
 * CI/CD Pipeline Development
 * Automated Testing