Cloud Infrastructure Engineer
CloudInfrastructureEngineer-DataPlatforms
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Cloud Infrastructure Engineer - Data Platforms at Cloud Infrastructure Engineer. Skills: Cloud Infrastructure Engineering, Data Platforms, AWS, Data Architecture, Infrastructure as Code, Cloud Automation, CI/CD, Data Processing, Data Orchestration, Python. Build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure. Build and maintenance of data architectures pipe”
What You'll Achieve.
ensure that all data is accurate, accessible, and secure; enable the transfer and processing of durable, complete and consistent data; manage the appropriate data volumes and velocity and adhere to the required security measures; fit for the intended data complexity and volumes; build the next-generation AWS data platform; operate a scalable, secure, and compliant cloud data platform on AWS that powers analytics, reporting, and data science; deliver robust data infrastructure and shared services, enabling data-driven insights and innovation while meeting rigorous security and regulatory standards; make it easy for data teams to use the platform consistently and safely; balance cost and speed; continuously improve reliability; achieving the goals of the business
Industry & Context.
Create solutions based on sophisticated analytical thought comparing and selecting complex alternatives; In-depth analysis with interpretative thinking will be required to define problems and develop innovative solutions; Adopt and include the outcomes of extensive research in problem solving processes
What They're Looking For.
Must Have
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 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
Nice to Have
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 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 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 with data platforms – including implementing robust observability, data quality checks, incident response processes, and conducting post-incident reviews to continuously improve reliability
What You'll Do.
Build and maintain the systems that collect
such as data pipelines
data warehouses and data lakes to ensure that all data is accurate
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
and operate a scalable
and compliant cloud data platform on AWS that powers analytics
and data science across our capital markets business
deliver robust data infrastructure and shared services
enabling data-driven insights and innovation while meeting rigorous security and regulatory standards
How You'll Work.
Team & Collaboration
Collaboration with data scientist to build and deploy machine learning models; Collaborate with data engineers, analytics teams, and business stakeholders; Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategies; lead collaborative, multi-year assignments and guide team members through structured assignments; identify the need for the inclusion of other areas of specialisation to complete assignments
Communication Scope
Advise key stakeholders, including functional leadership teams and senior management on functional and cross functional areas of impact and alignment
Process & Methodology
Plan resources, budgets, manage and maintain policies, deliver continuous improvements, escalate breaches of policies/procedures, planning for the department’s future needs and operations, balancing short and long term goals, ensuring that budgets and schedules meet corporate requirements, managing risk, strengthening controls
Full Job Description
# **Job Description** **Purpose of the role** To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure. **Accountabilities** * 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. **Vice President Expectations** * To 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.. * If managing a team, they define jobs and responsibilities, planning for the department’s future needs and operations, counselling employees on performance and contributing to employee pay decisions/changes. They may also lead a number of specialists to influence the operations of a department, in alignment with strategic as well as tactical priorities, while balancing short and long term goals and ensuring that budgets and schedules meet corporate requirements.. * If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.. * OR for an individual contributor, they will be a subject matter expert within own discipline and will guide technical direction. Th
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