Cloud Infrastructure Engineer - Data Platforms

Barclays•Jefferson, CO

6d•$170,000 - $230,000•Onsite

About The Position

Embark on a transformative journey as a Cloud Infrastructure Engineer – Data Platforms. Join our team and use your cloud architecture expertise to build the next-generation AWS data platform for a leading financial institution. In your role as role, you will design, build, and operate a scalable, secure, and compliant cloud data platform on AWS that powers analytics, reporting, and data science across our capital markets business. You’ll collaborate with data engineers, analytics teams, and business stakeholders to deliver robust data infrastructure and shared services, enabling data-driven insights and innovation while meeting rigorous security and regulatory standards.

Requirements

AWS-native Data Platforms: Designing, building, and operating production-grade AWS data platform infrastructure (e.g. S3-based data lakes, lakehouse architectures, metadata catalogs) and delivering shared data services for analytics and machine learning at scale
Infrastructure as Code: Defining and managing cloud environments using infrastructure-as-code tools such as Terraform, CloudFormation, or equivalent frameworks, ensuring repeatable and version-controlled provisioning of AWS resources
Cloud Automation & CI/CD: Developing cloud automation and CI/CD pipelines for deploying data platform components, with an automation-first mindset to streamline releases, environment promotion, and operations
Data Processing & Orchestration: Building batch and streaming data pipelines using cloud-native processing frameworks and orchestration tools – for example, Apache Spark on AWS (EMR) or AWS Glue for big data jobs, and workflow orchestrators like Apache Airflow or AWS Step Functions to manage complex data workflows
Python & Tooling: Strong Python programming skills for developing platform automation, internal tools, and system integrations. Ability to script and automate tasks, build custom utilities, and interface with AWS services and APIs in Python
Security & Networking Fundamentals: Solid understanding of AWS networking and security (VPC design, IAM, data encryption, key management, etc.) and experience implementing data access controls and compliance measures in a regulated enterprise environment
Platform Engineering Mindset: Familiarity with platform engineering concepts – for instance, building self-service data infrastructure and “paved road” frameworks that make it easy for data teams to use the platform consistently and safely
Cost Optimization & Performance: Experience with cost management, performance tuning, and capacity planning for large-scale cloud data systems. Ability to monitor usage, right-size resources, and optimize data processing jobs to balance cost and speed
Operational Excellence: Commitment to operational excellence with data platforms – including implementing robust observability, data quality checks, incident response processes, and conducting post-incident reviews to continuously improve reliability
Risk and controls
Change and transformation
Business acumen
Strategic thinking
Digital and technology
Job-specific technical skills

Nice To Haves

Other highly valued skills may include: Security & Networking Fundamentals: Solid understanding of AWS networking and security (VPC design, IAM, data encryption, key management, etc.) and experience implementing data access controls and compliance measures in a regulated enterprise environment
Platform Engineering Mindset: Familiarity with platform engineering concepts – for instance, building self-service data infrastructure and “paved road” frameworks that make it easy for data teams to use the platform consistently and safely
Cost Optimization & Performance: Experience with cost management, performance tuning, and capacity planning for large-scale cloud data systems. Ability to monitor usage, right-size resources, and optimize data processing jobs to balance cost and speed
Operational Excellence: Commitment to operational excellence with data platforms – including implementing robust observability, data quality checks, incident response processes, and conducting post-incident reviews to continuously improve reliability

Responsibilities

Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
Development of processing and analysis algorithms fit for the intended data complexity and volumes.
Collaboration with data scientist to build and deploy machine learning models.
Contribute or set strategy, drive requirements and make recommendations for change.
Plan resources, budgets, and policies; manage and maintain policies/ processes; deliver continuous improvements and escalate breaches of policies/procedures.
Design, build, and operate a scalable, secure, and compliant cloud data platform on AWS that powers analytics, reporting, and data science across our capital markets business.
Collaborate with data engineers, analytics teams, and business stakeholders to deliver robust data infrastructure and shared services, enabling data-driven insights and innovation while meeting rigorous security and regulatory standards.