Azure Data Engineer

KDR Talent Solutions
Manchester
2 months ago
Applications closed

Related Jobs

View all jobs

Azure Data Engineer - Insurance Firm – London – hybrid working

Azure Data Engineer (AI / ML)

Azure Data Engineer

Data Engineer - Python & Azure

Data Engineer (Databricks & Azure) - Clean Energy

Data Engineer - Remote (UK ONLY) - Outside IR35

This range is provided by KDR Talent Solutions. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

Direct message the job poster from KDR Talent Solutions

Data & Analytics Recruiter - Supporting companies in hiring the best professionals in Data, Digital & Analytics

Azure Data Engineer | Location: Manchester (Hybrid - 1-2 days a month in office) | Salary: £55,000-£65,000

The Opportunity

Are you passionate about building cutting-edge data platforms that drive business growth? Our client is seeking a skilled and motivatedData Engineerto play a key role in the creation of abrand-new data platformwithin theMicrosoft Azure and Fabricecosystem.

This is an exciting opportunity to be at the forefront of data innovation, working within a newly formedData & Reportingteam. You’ll help shape the data strategy, improve data quality, and empower the business to make data-driven decisions.

As a Data Engineer, you'll work closely with both technical and business stakeholders, leveraging your expertise to design, develop, and optimize ahigh-performance data platform. This platform will be built to scale, incorporating the latest advancements in data intelligence while supporting strategic business objectives.

Key Responsibilities

  1. Build & Develop– Design and maintain a robustAzure-based Data Platform, ensuring performance, scalability, and availability.
  2. Data Pipelines– Connect APIs, databases, and data streams to the platform, implementing ETL/ELT processes.
  3. Data Integrity– Embed quality measures, monitoring, and alerting mechanisms.
  4. CI/CD & Automation– Create deployment pipelines and automate workflows.
  5. Collaboration– Work with stakeholders acrossGlobal IT, Data, and Reportingteams to translate business requirements into technical solutions.
  6. Futureproofing– Drive the evolution of the data platform, ensuring adaptability for new data sources, analytical models, and emerging technologies.

What You’ll Bring

  1. Extensive hands-on experiencewithMicrosoft Azure data tools(must-have: Azure Data Factory, Azure Synapse, or Azure SQL).
  2. Dimensional modellingexpertise for analytics use cases.
  3. Python scriptingexperience for data automation.
  4. Experience withCI/CD methodologiesfor data platforms.
  5. Knowledge ofMS SQL Server, SSIS, Visual Studio, and SSDT projects.
  6. Hands-on experience withMicrosoft Fabric.
  7. Familiarity withSalesforceand/orWorkday.
  8. Previous experience in a relevant industry.

Why Join?

  1. Greenfield Project– Work on an all-new data platform, shaping its architecture from the ground up.
  2. Collaborative Culture– Engage with global teams in an agile, innovative environment.
  3. Career Growth– Play a pivotal role in driving data excellence within a forward-thinking business.
  4. Cutting-Edge Tech– Work with the latest advancements inAzure, Fabric, and Data Engineering.

This is a fantastic opportunity for aData Engineerlooking to make a tangible impact. If you’re ready to take on a challenging and rewarding role, apply today!

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Information Technology

Industries

Data Infrastructure and Analytics, IT System Data Services, and IT Services and IT Consulting

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Jobs

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Jobs Careers

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Jobs

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

🍪 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.