Position:

Principal Cloud Engineer Brambles Digital – UK or Madrid

Position Purpose

Cloud Engineers set up, operate, develop, evolve, and maintain cloud-centric platform(s) including IoT, Customer Solution Delivery, Data Science Environments and Software Delivery, as well as satellite tools and environments (file storage, databases, TensorFlow, data streaming, eventing, machine learning and big data frameworks, notebook technologies, and container orchestration tooling).

Location:UK or Madrid.

Measures:

Successful roll out, development and continuous evolution and operation of cloud-based data science and machine learning platforms, IoT, Edge and Software Delivery frameworks, both for research & development and for continuous operation

Effective support of data science projects, digitisation projects and software delivery projects

Reliability of systems

Adoption of systems

Major/Key Accountabilities

Design, develop, release and operate bespoke software tools, DSLs, parsers, libraries, frameworks, and services, through development in system languages such as Go, C, C++, Rust etc which are used by application software development teams and software operations teams. Currently the majority of this code is written in Go.

Engage with all development teams, to improve and extend the existing tooling. The purpose is to enable them to scale, and focus on feature development, by providing a common technical platform / infrastructure, and improving developer experience.

Develop, release, operate software tools, libraries, frameworks and services written in mainly in Go

Design new tools and improve/extend existing ones by engaging with key stakeholders and team leads

Support and train tool users in application development and software operations teams

Responsible for learning, operating, evolving, contributing to, and maintaining Brambles Digital bespoke frameworks that support the BRIX platform (such as specific data pipes and bespoke terminal applications and REPLs)

Responsible for rigorous testing of tools, libraries, frameworks, and services robustness and scalability

Will contribute to data science / engineering teams discussions, providing insight as needed on other team member’s current approaches and methods as well as on tools and data repositories

Liaise with Brambles Digital Cloud Engineering / BRIX digital operations, in order to understand current software operation and operation tooling

Build and maintain Continuous Integration and Continuous Deployment pipelines used in the release process

Operate and monitor software services running in a Kubernetes environment, and natively on AWS relevant to bespoke software tools, DSLs, parsers, libraries, frameworks, and services.

Responsible for contributing to capability building of the team, including researching and staying up-to-date on best practices e.g. Platform Engineering

Create user documentation, troubleshooting guides, FAQs for software tools, and services.

Qualifications

Essential

BS degree in Data Science, Computer Science, Engineering, Math, Statistics, Physics, or similar formal training or equivalent

Desirable Qualifications

Proven experience with looking after data recovery and database high availability and database tuning

Proven experience with FinOps and being able to optimise spend for CE impact

Experience with working with IoT and Edge interaction with the Cloud

Experience

5yr relevant experience in Cloud Engineering or adjacent fields

1. Installed, operated, and managed several data science and machine learning frameworks, or developed own data science methodologies
2. Experience with Continuous Integration and Continuous Deployment
3. Experience operating, optimising, querying, and administering databases (such as Postgres, TimescaleDB, etc.)
4. Comfortable using and working in a polyglot computer language environment (Python, Go, Julia etc.)
5. Experience with Amazon Web Services (S3, EKS, ECR, EMR, etc.)
6. Experience with containers and orchestration (e.g. Docker, Kubernetes)
7. Experience with Big Data processing technologies (Spark, Hadoop, Flink etc)
8. Experience with interactive notebooks (e.g. JupyterHub, Databricks)
9. Experience with Git Ops style automation
10. Experience with *ix (e.g, Linux, BSD, etc.) tooling and scripting
11. Participated in projects that are based on data science methodologies, and/or physical experiments, or statistical analysis – especially in a data engineer and dev ops capacity.

Skills and Knowledge

1. Knowledge of major data science and dev ops frameworks and methods
2. Very strong analytical skills and systems thinking
3. Strong programming skills in addition to operational skills a plus (ideally in one or more of the following languages: Python, Go, Julia, or C/C++)
4. Attention to big picture and details