Data Architect

Amtis - Digital, Technology, Transformation
West Midlands
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Amtis - Digital, Technology, Transformation provided pay range

This range is provided by Amtis - Digital, Technology, Transformation. 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 Amtis - Digital, Technology, Transformation

Helping Data-Driven Businesses Hire Elite Talent in Data, Analytics, AI & ML 🚀 | Global Specialist Recruiter | Specialist in Senior & Strategic Hires

Permanent

Up to £90,000

Overview:

We’re looking for a Data Architect to design and deliver modern data solutions across an Azure ecosystem. You’ll play a key role in shaping the data strategy, owning the architecture of scalable, secure, and high-performing data platforms that enable advanced analytics and business insights.

Key Responsibilities:

  • Lead the design and architecture of end-to-end data platforms using Azure services (e.g., Azure Data Lake, Data Factory, Synapse, Fabric, Key Vault).
  • Architect and optimise Databricks environments for data engineering & analytics.
  • Develop data models, standards, and best practices ensuring data quality, governance, and reliability.
  • Collaborate with data engineers, analysts, and business stakeholders to translate requirements into scalable solutions.
  • Oversee data integration, ingestion, and transformation pipelines across batch and streaming workloads.
  • Ensure security, compliance, and cost optimisation throughout the data estate.

Skills & Experience:

  • Strong experience architecting cloud-native data solutions on Microsoft Azure.
  • Hands-on expertise with Databricks (Delta Lake, Spark, notebooks, cluster management).
  • Deep understanding of data modelling, warehousing, and distributed data processing.
  • Experience with Python/SQL for data engineering and solution design.
  • Familiarity with CI/CD, DevOps, and Infrastructure-as-Code (Terraform/ARM/Bicep) is beneficial.

If you're interested in the opportunity, please apply with your updated CV and contact information.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Information Technology

Industries

IT Services and IT Consulting

Location: West Midlands, England, United Kingdom


#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.

Careers Jobs

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.

Jobs

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.

Education Jobs

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.

🍪 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.