Senior Data Engineer - Azure Data - Burton-on-Trent - Hybrid

Crimson Limited
Burton-on-Trent
3 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Azure Data - Burton-on-Trent - Permanent - Hybrid


Salary - £60,000 - £67,000 per annum


This role requires 1 day / week in Burton-on-Trent, with hybrid working arrangements.


Our client is seeking a highly skilled Senior Data Engineer to join their dynamic IT team, based in Burton-on-Trent. The Senior Data Engineer will come on board to support the Strategic Data Manager in establishing and managing an efficient Business Intelligence technical service. Assisting in the advancement of our cloud-based data platforms, providing options for timely processing and cost-efficient solutions. A strong background in Azure Data Pipeline development is key for this position.


Key Skills & Responsibilities:

  • Build and manage pipelines using Azure Data Factory, Databricks, CI/CD, and Terraform.
  • Optimisation of ETL processes for performance and cost-efficiency.
  • Design scalable data models aligned with business needs.
  • Azure data solutions for efficient data storage and retrieval.
  • Ensure compliance with data protection laws (e.g., GDPR), implement encryption and access controls.
  • Work with cross-functional teams and mentor junior engineers.
  • Manage and tune Azure SQL Database instances.
  • Proactively monitor pipelines and infrastructure for performance and reliability.
  • Maintain technical documentation and lead knowledge-sharing initiatives.
  • Deploy advanced analytics and machine learning solutions using Azure.
  • Stay current with Azure technologies and identify areas for enhancement.
  • Databricks (Unity Catalog, DLT), Data Factory, Synapse, Data Lake, Stream Analytics, Event Hubs.
  • Strong knowledge of Python, Scala, C#, .NET.
  • Experience with advanced SQL, T‑SQL, relational databases.
  • Azure DevOps, Terraform, BICEP, ARM templates.
  • Distributed computing, cloud‑native design patterns.
  • Data modelling, metadata management, data quality, data as a product.
  • Strong communication, empathy, determination, openness to innovation.
  • Strong Microsoft Office 365 experience

Interested?

Please submit your updated CV to Lewis Rushton at Crimson for immediate consideration.


Not interested?

Do you know someone who might be a perfect fit for this role?


Refer a friend and earn £250 worth of vouchers!

Crimson is acting as an employment agency regarding this vacancy.


#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.

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

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

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.