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

Apply Now

Senior Data Engineer

Xcede
London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

London - 2 days a week in the office

Up to £95k


A high-growth digital platform is transforming a multi-billion-dollar global industry through innovative online technologies. The platform connects a large network of customers and partners worldwide, powering billions in annual transactions. With significant revenue growth and international reach, the company is modernising one of the last sectors to fully embrace digital transformation, building a global, category-defining business in the process.

As the organisation scales its AI and machine learning capabilities across search, recommendations, and analytics, it is investing heavily in robust data infrastructure to enable rapid experimentation, reliable insights, and data-driven decision-making. The Senior Data Engineer will collaborate with Product Managers, ML Engineers, Analysts, and Software Engineers to design and maintain the data pipelines and infrastructure that power AI-driven features and business intelligence, handling millions of events and requests daily.


Key Responsibilities:

  • Design, build, and maintain reliable ETL/ELT pipelines to support analytics, ML models, and business intelligence
  • Develop scalable batch and streaming data pipelines to process millions of events and transactions daily
  • Implement workflow orchestration using Airflow, Dagster, or similar tools
  • Build data validation and quality monitoring frameworks to ensure data accuracy and reliability
  • Collaborate cross-functionally with Software, ML, and Analytics teams to deliver production-ready data products
  • Mentor junior engineers and contribute to engineering best practices


Required Skills & Experience:

  • 5+ years of experience building and maintaining data pipelines in production environments
  • Strong Python and SQL skills (Pandas, PySpark, query optimisation)
  • Cloud experience (AWS preferred) including S3, Redshift, Glue, Lambda
  • Familiarity with data warehousing (Redshift, Snowflake, BigQuery)
  • Experience with workflow orchestration tools (Airflow, Dagster, Prefect)
  • Understanding of distributed systems, batch and streaming data (Kafka, Kinesis)
  • Knowledge of IaC (Terraform, CloudFormation) and containerisation (Docker, Kubernetes)


Nice to have:

  • Experience with dbt, feature stores, or ML pipeline tooling
  • Familiarity with Elasticsearch or real-time analytics (Flink, Materialize)
  • Exposure to eCommerce, marketplace, or transactional environments

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.