Data Architect

Berg Search
Birmingham
1 year ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Role Overview


As aData Architect / Data Engineer, Integrations, you will tackle the challenges of building reliable, scalable data pipelines and integrations in complex, enterprise-scale environments. You will design, build, and optimize solutions that handle large-scale, inconsistent, and unstructured data, ensuring clean, structured, and secure data to power intelligent systems. You will also enable cloud-agnostic data storage and processing solutions to support client-specific environments, including multi-cloud, on-premises, or dedicated tenancies. Collaborating closely withResearch Engineers and Research Scientists, you will ensure data is modelled effectively to support advanced retrieval, real-time insights, and AI-enabled decision support.

Key Responsibilities

  • Architect and Build: Design, implement, and optimize scalable data pipelines and architectures to process high-velocity, large-scale data from diverse sources.
  • Integrate Diverse Systems: Develop and maintain integrations across enterprise platforms, project tools, communication systems, and external APIs to enable seamless bi-directional data flow across the organization.
  • Enhance Data Quality: Design processes to validate, transform, and structure inconsistent and unstructured data, ensuring it is clean, reliable, and optimized for downstream use.
  • Collaborate Across Teams: Work closely withResearch Engineers and Research Scientiststo ensure data is structured effectively to support advanced retrieval, query performance, and AI-enabled insights.
  • Leverage Best-in-Class Tools: Build and optimize streaming data pipelines using Azure tools such as Event Hubs, Stream Analytics, and Data Explorer to deliver real-time data insights, risk detection, and proactive alerting.
  • Utilize Cloud Expertise: Leverage Microsoft Azure services for secure, cost-effective, and high-performance data solutions, while enabling cloud-agnostic data processing and storage solutions to meet client-specific requirements, including AWS, GCP, or on-premises environments.
  • Ensure Security and Compliance: Implement role-based access controls, encryption, and logging to ensure data security and compliance with standards such asISO 27001, SOC 2, and GDPR.
  • Drive Efficiency: Identify opportunities to improve data flow, processing times, and cost-effectiveness across the data infrastructure.
  • Stay Current: Keep up to date with emerging trends in cloud platforms, data engineering, and real-time processing technologies to future-proof the infrastructure.


Expertise and Skills


Core Technical Competencies:

  • Cloud Platforms: Expertise in Microsoft Azure (Event Hubs, Stream Analytics, Data Explorer, Synapse) with a strong understanding of AWS, GCP, and the ability to deliver cloud-agnostic and on-premises data solutions.
  • Data Engineering Tools: Experience with Databricks, Apache Spark, or similar frameworks for large-scale data processing.
  • ETL/ELT Pipelines: Proven ability to design and manage scalable data pipelines using tools like Azure Data Factory, dbt, or Apache Airflow.
  • Programming Proficiency: Advanced proficiency in Python and SQL for building and automating data processing workflows.


Data Architecture & Integration:

  • Data Modelling: Experience designing relational, non-relational, and graph-based data models with strong permisioning and access control structures.
  • System Integrations: Familiarity with integrating enterprise tools (e.g., ERP, project management, document stores) and working with APIs or streaming frameworks.
  • Real-Time Processing: Practical experience with streaming technologies likeAzure Event Hubs, Stream Analytics, or other real-time tools.


Security & Compliance:

  • Best Practices: Experience implementing encryption, logging, and role-based access control to align with standards such asISO 27001, SOC 2, and GDPR.
  • Data Governance: Understanding of data lineage, cataloguing, and governance frameworks.


Mindset & Approach:

  • Problem Solver: Thrives on tackling complex data challenges and delivering robust, scalable solutions.
  • Collaborative Partner: Works effectively across teams, particularly withResearch Engineers and Research Scientists, to align data infrastructure with business and technical needs.
  • Detail-Oriented: Committed to ensuring high data quality, security, and performance.
  • Continuous Learner: Eager to explore emerging tools and techniques to push the boundaries of data engineering.


What Success Looks Like


Success in this role will be measured by the robustness and efficiency of our data pipelines, the seamless integration of diverse systems, and the ability to deliver clean, secure, and well-structured data. Your solutions will support client-specific needs, enabling processing and storage across cloud-agnostic, multi-cloud, or on-premises environments. Your work will directly power intelligent, AI-enabled insights and decision-making for enterprise-scale programmes.

What We Offer

  • Competitive salary
  • Bonus scheme
  • Wellness allowance
  • Fully remote working (with regular company get-togethers)
  • Private medical and dental insurance*
  • Life assurance, critical illness cover, and income protection*

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.