Principal Cloud DevOps Engineer

Trafigura Group
London
11 months ago
Applications closed

Related Jobs

View all jobs

Principal Consultant - Data Engineering Lead

Data Architect

Data Architect

Principal Consultant- Data Architect, Oracle HCM Cloud, Oracle EBS

Senior Data Architect: Enterprise Cloud & Data Strategy

Senior Data Architect — Oracle HCM Cloud & EBS Migrations

Technical Lead – Cloud DevOps

Apply locations London, United Kingdom

Time type Full time

Posted on Posted Yesterday

Job requisition id R-014302

Main Purpose:

  • We are recruiting an experienced Cloud Platform DevOps Lead to manage the team of DevOps engineers and support the ongoing design, development and management of an advanced data and analytics platform on AWS.
  • Work on POCs and solution architecture for new platform components.
  • Provisioning and automation of AWS Infrastructure using IaC.
  • Collaborate with data engineers and data scientists while working on CI/CD pipelines and MLOps.
  • Implement platform observability, security and telemetry to ensure proactive monitoring and alerting of Cloud applications.
  • Application and environment support for cloud infrastructure.

Knowledge Skills and Abilities, Key Responsibilities:

Technical Skillsets:

  • Strong experience in containerization and container orchestration engines like ECS, EKS.
  • Experience with Cloud Native ecosystem, K8s and GitOps continuous delivery tools.
  • Experience with programming using Python, Bash.
  • Experience working with IaC frameworks like AWS CDK, Terraform, Cloud Formation.
  • Knowledge of configuration management tools like Ansible or equivalent.
  • Strong experience working with AWS IAM, data and analytics services.
  • Experience with automation, monitoring, implementing CI/CD.
  • Experience in building container images securely and image lifecycle management.
  • AWS certifications are a plus.

Experience:

  • Minimum 5-10 years of hands-on Cloud DevOps experience.
  • Minimum 2-5 years of technical leadership experience.
  • Bachelor’s degree or higher in engineering.
  • Experience of 2-4 years in Investment Bank is preferred.

Competencies:

  • Team management and mentorship.
  • Strong oral and written communications with strong inter-personal skills.
  • Strong analytical and problem-solving skills.
  • Capable of working in groups as well as independently.
  • Professional management of employee relationships at all levels.
  • Ability to maintain the confidentiality of sensitive information.
  • Team player with an enthusiastic approach to fresh challenges.

Key Relationships and Department Overview:

Key Relationships:

  • Internal: Technical and Functional stakeholders based in Geneva, UK and other Trafigura locations. Work closely with data engineers and data scientists.
  • External: Strategic outsourcing partners.

Department:The Data Science and Engineering team researches, develops and provides advanced analytics and data services to the trading business and other commercial operations at Trafigura. It is comprised of Data Scientists, Data Engineers and Quantitative Finance experts. It is a commercially driven, front office aligned team, that works in close partnership with the trading desks, global research and enterprise technology.

Equal Opportunity Employer:We are an Equal Opportunity Employer and take pride in a diverse workforce. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, colour, religion, gender, sexual orientation, national origin, age, marital or veteran status, medical condition or handicap, disability, or any other legally protected status.

J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.