Lead Big Data Ops Engineer

Hunter Bond
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Architect - Bristol - Hybrid Opportunity

Lead Data Engineer (AWS & Snowflake)

Lead Data Scientist - Remote

Lead Data Scientist - Remote

Lead Data Engineer

Lead Data Engineer

My leading Tech client are looking for a talented and motivated individual to ensure the resilience, performance, and cost-effectiveness of their Azure-based data platform. This role is essential to their data ecosystem, combining platform reliability, incident response, SLA management, cost optimization (FinOps), and deployment oversight.

You will be the single point of contact for operational issues, driving rapid resolution during outages, leading communications with stakeholders, and shaping the processes that keeps their platform running smoothly and efficiently.

This is a newly created role in a growing business. A brilliant opportunity!

The following skills/experience is required:

  • Proven operational leadership for large-scale data platforms.
  • Expertise in incident management, SLA enforcement, and stakeholder communication.
  • Hands-on experience with Azure Synapse, Databricks, ADF, Power BI.
  • Familiarity with CI/CD and automation.
  • Strong FinOps mindset and cost management experience.
  • Knowledge of monitoring and observability frameworks.

Salary: Up to £90,000 + bonus + package

Level: Lead Engineer

Location: London (good work from home options available)

If you are interested in this Lead Big Data Ops Engineer position and meet the above requirements please apply imme...

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.

Products

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Jobs

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

🍪 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.