Senior DevOps Engineer

DataCamp
1 year ago
Applications closed

Related Jobs

View all jobs

Senior MLOps Engineer

Senior MLOps Engineer

Senior MLOps Engineer

Senior Machine Learning Engineer

Senior Lead Analyst - Data Science_ AI/ML & Gen AI

Senior Lead Analyst - Data Science_ AI/ML & Gen AI - UK

About DataCamp

There is incredible power in data and AI—but only if you know what to do with it. DataCamp teaches companies and individuals the skills to work with data and AI in the real world. Our mission is to democratize data and AI skills for everyone! 

Companies and teams of every size use DataCamp to close their skill gaps and make better data-driven decisions. We work with over:

4000+ companies 3000+ academic organizations 12+ million DataCamp learners

And a global learning community spread across 180+ countries.

At DataCamp, we believe that everyone deserves access to high-quality education and data and AI skill development for a more secure future. From our first-class courses, projects, code-alongs, certification programs, and DataLab—we are an all-in-one platform on a mission to democratize data and AI education for all.

About the role

DataCamp's infrastructure team, which is part of the Platform Engineering department, is a T-shaped cross functional team that looks after CI/CD pipelines, cloud infrastructure (deployed on AWS), logging, monitoring and security. The infrastructure team also looks after the data platform (deployed on GCP) as we have data engineers embedded in our cross functional infrastructure team. The team helps advise our production engineering teams on infrastructure best practices on all DataCamp projects and looks after the whole DataCamp Platform to ensure commercial availability for our customers.

To facilitate this we have a highly automated CI/CD pipeline based on CircleCI and Spotify Backstage (internal engineering portal) which allows developers to ship what they build, increasing deployment speed and ownership and visibility. The infrastructure team aims to enhance developer productivity, scalability, availability and security by providing feedback cycles for teams so they follow a model of continuous improvement. 

It will be your role as a part of the Infrastructure team to enable the development teams to deploy their applications as seamlessly as possible and also advise them on either new content for DataCamp courses or any new projects that require infrastructure expertise. You will be managing company wide shared resources which support our microservice architecture, and building upon internal services. The team has a strong bias towards providing self-serve and automation for deployment/infrastructure provisioning as well as cost control and ensuring security standards. The infrastructure team aims to support other teams using these services rather than being a central bottleneck in the company. You will play a key part in planning future improvements and owning your day to day work.

All DataCamp Platform workloads are deployed on EKS (Kubernetes) and our Istio service mesh by the self-service deployment pipelines. All infrastructure is provisioned using Terraform. The infrastructure team also manages the Kong API Gateway allowing external ingress traffic into the DataCamp Platform. The DataCamp Data Platform is deployed on Big Query and airflow manages our data pipeline jobs.

As well as providing means for other development teams to deploy their applications as seamlessly as possible, the infrastructure team takes ownership of the our learn multiplexer product that schedules course sessions for learners on the DataCamp platform. 

The ideal candidate

Has 2+ years of administering/maintaining infrastructure related tools (AWS, Docker, K8s) Has 2+ years experience advising on/implementing deployment pipelines (CI/CD) Has 2+ years of web development experience (javascript, go, python, node, ruby)

Has 2+ years of security tooling experience (sonarcloud, vulnerability scanning tooling)

You have experience with Infrastructure-as-code (Terraform, Ansible, etc) Has excellent oral and written communication skills Is interested in understanding and scaling complex systems Is interested in monitoring and self healing systems Is highly organized with a flexible, can-do attitude and a willingness/aptitude for learning Improves the team with code reviews, technical discussions and documentation Is able to work collaboratively in teams and develop meaningful relationships to achieve common goals

It's a plus if

You have an understanding of data engineering principles You have experience with API-gateways or service meshes (Kong, Istio, etc) You are passionate about data science and education

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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

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

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.