Data Ops Engineer

Sofia
11 months ago
Applications closed

Related Jobs

View all jobs

Data/Machine Learning Ops Engineer

Machine Learning Engineer

Data Scientist, Machine Learning Engineer, Data Analyst, Data Engineer, AI Engineer, Business Intelligence Analyst, Data Architect, Analytics Engineer, Research Data Scientist, Statistician, Quantitative Analyst, ML Ops Engineer, Applied Scientist, Insigh

Senior Data Engineer (AI & MLOps, AWS, Python)

Machine Learning Engineer

Machine Learning Engineer

Data Ops Engineer | Data Tooling, Security | FinTech Software Company

Hybrid in Sofia
£85-90,000 
Our client is looking for a UX/UI Developer to join a top-tier, well-established FinTech firm specialising in SaaS products that deliver real-time market data and pricing, comparable with industry giants like Bloomberg and Reuters. It has more than 600 employees spread across global locations in the UK, US, China, India, Singapore, Brazil, Belgium, Finland and beyond.
 
We are looking for an experienced Data Ops Engineer to lead the implementation of best practices in DataOps and optimise our client’s Snowflake platform. You will play a key role in managing data resilience, performance, and security while ensuring efficient user and role management.
 
You will also support data orchestration using Dagster (or similar tools like Airflow) and enhance integration with Qlik for operational analytics. This role is crucial in modernising their data infrastructure and ensuring high availability, reliability, and integrity of data platforms.
 
This is a fantastic opportunity to drive real change, collaborate with teams across Data, Engineering, and Cyber, and help shape their next-generation data architecture.
 
Key skills:

DataOps best practices
Snowflake, including performance tuning, governance, and user/role management
Dagster, Airflow, or Python-based orchestration tools
Qlik for data visualisation and analytics
Experience with data backup, restore, and integrity management
Proficiency in databases such as Cosmos DB, MySQL, and SQL Server
RBAC and user management using Azure Active Directory (AD)
Monitoring and observability tools (e.g., Grafana)
Scripting and automation with Bash, PowerShell, and Linux administration
Strong problem-solving and collaboration skills 
Nice to have skills:

Cloud deployment experience (Azure preferred, but AWS or GCP acceptable)
Experience with data pipelines and streaming data technologies
Kubernetes, Docker, and containerised data platforms
Familiarity with SQL Managed Instances for data system administration
Understanding of Azure cybersecurity best practices
Experience with Terraform, GitHub, and infrastructure as code
CI/CD experience with Azure DevOps or similar tools 
Projects & Responsibilities:

Optimise and manage Snowflake for performance, resilience, and security
Develop and implement DataOps best practices to enhance efficiency
Support data orchestration with Dagster (or similar tools)
Ensure data integrity and recoverability, implementing strong backup and restore processes
Monitor and troubleshoot data platforms, using tools like Grafana
Collaborate across teams (Data, Engineering, Cyber) to drive operational improvements 
Benefits:

Highly flexible hybrid working
Option to work remotely from anywhere in the world during August
25 days holiday, 3 extra days at Christmas, 2 volunteering days
Pension contribution
Medical insurance
Life insurance
Virtual GP service
Health cash plan 
If you are excited by the prospect of this role, please get in touch quickly as our client is looking to move quickly!
Data Ops Engineer | Data Tooling, Security | FinTech Software Company

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.

Careers

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

Jobs

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

Education

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.