Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer

Canopius
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

We are seeking a motivated Data Engineer to support the development, and maintenance of a data-bricks lake house architecture. This role is critical in enabling the organisation to store, integrate, and analyse large volumes of data from diverse sources, ensuring data is accessible, accurate, and actionable for decision-making.

Data Architecture & Engineering
  • Contribute to the design, development, unit testing, and deployment of data pipelines for extraction, ingestion, and transformation of data.
  • Support the development of scalable data architecture to process and store datasets efficiently.
  • Optimisation of data pipelines from internal and external sources using modern data engineering tools and frameworks.
  • Ensure their deliverables meet the team standards and best practices.
Collaboration & Stakeholder Engagement
  • Work closely with data scientists, analysts, and business stakeholders to understand data needs and deliver fit-for-purpose datasets and operational data capabilities.
  • Collaborate with team members to support applications and resolve technical challenges.
  • Create and maintain high-quality documentation
Data Quality & Automation
  • Optimize automated data quality, cleansing, and validation processes within data pipelines.
Technical Proficiency
  • Experience in data warehousing, data lakes and data integration, transformation, and modelling
  • Hands-on experience with Databricks for scalable data processing and Azure SQL for data storage and querying.
  • Proficiency in SQL, Python, and modern data pipeline tools (e.g. dbt).
Data Governance & Quality
  • Demonstrate an ability to implement data quality frameworks and ensure data integrity across systems.
  • Familiarity with data privacy regulations (e.g., GDPR) and security best practices.
Security & Compliance
  • Follow best practices for data security and privacy, ensuring compliance with internal policies and external regulatory requirements.
  • Apply technical knowledge to deliver key projects, including the automation of data deployment, release, and upgrade processes.
Cross-functional Collaboration
  • Experience working in cross-functional teams and collaborating with other engineers.
Certifications
  • Databricks or Azure Data Engineer certifications (e.g. Databricks Certified Data Engineer Associate or Databricks Certified Data Engineer professional)
  • DBT Analytics Engineering Certification


#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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.