Data Architect

Ocho

Belfast

1 year ago

Applications closed

Related Jobs

View all jobs

Data Engineering Leader: Scale & Architect Platforms

GCP Data Architect & Big Data Engineer

Senior Data Engineer: Architect Scalable Data Platforms

EMEA Lead Data Engineer & Architect

Senior Data Engineer: Tibco & Python ETL Architect

Lead Data Engineer — Hybrid, 4‑Day Week, Scalable Data Architecture

My client is transforming their business by embedding data analytics into all aspects of their operations, empowering clients across diverse sectors to drive meaningful change. Right now, theyre particularly focused on bringing on board experienced Managers to enhance their Belfast teams impressive capabilities, with a strong emphasis on Database Architecture expertise combined with Enterprise Scale Azure competencies. Role Description: - Lead the integration of advanced data analytics into existing services, extracting maximum value from information assets by partnering with domain experts. - Manage and inspire diverse teams using a range of Data and Analytics technologies, with a focus on leveraging Azure for cloud data architecture and solutions. - Adapt leadership style to fit varied team and client needs, anticipating risks and fostering a collaborative learning environment. - Collaborate with cross-functional teams across industries to aid clients in transformative business strategies. - Bring a passion for team growth, adding energy, and leading others while cultivating a culture of excellence. - Leverage experience to offer unique insights that deliver value to clients and broader society. - Drive operational efficiency and execute high-quality projects that consistently meet goals. - Shape the future of the Data & Analytics team by building the brand, attending events, sharing thought leadership, and collaborating internally. - Build and sustain key relationships to identify new business opportunities. Skills and Attributes for Success: - Consulting or industry experience with significant management and team development expertise. - Proven ability to communicate technical information effectively to non-technical stakeholders. - Solid background as a Data Architect, especially in Azure cloud architecture, with strong knowledge of both relational and non-relational databases. - Skilled in cloud-based data architecture design, including conceptual, logical, and physical levels. - Experience across the data analytics lifecycle, especially Cloud and Data Architecture, Data Pipelines, ETL/ELT, and Data Governance. - Relevant degree or equivalent professional experience, with Azure or AWS cloud certifications as a bonus. Package Details: Offering a competitive package that acknowledges individual and team performance, my client provides a comprehensive Total Rewards package with flexible working options, career development, holidays, health benefits, insurance, and a variety of discounts. Continuous Learning: This role offers an opportunity to build the skills needed to navigate challenges and customize your career path. To apply, please send your CV via the link below or connect directly with Ryan Quinn on LinkedIn. Skills: Sql Server Azure Database Benefits: Bonus performance reviews hybrid

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

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

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Jan 29, 2026

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Jan 24, 2026

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.