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

Apply Now

Principal Data Engineer

Ocho
Belfast
3 days ago
Create job alert
Principal Data Engineer

Location: Belfast | Hybrid
Type: Permanent | Full-time


Overview

We’re hiring an experienced Data Engineer to help design and build scalable, high-performance data platforms that power decision-making, analytics, and automation across global trading operations.


This is a hands-on engineering role within a collaborative, technology-driven environment. You’ll take ownership of data systems that enable fast, accurate insights ,supporting teams across trading, risk, and operations.


The Opportunity

You’ll play a key part in evolving a modern data stack, integrating multiple data sources, building robust pipelines, and optimising data flows that support analytics, machine learning, and real-time decision systems.


You’ll work across engineering, trading, and analytics teams to ensure data quality, reliability, and scale, while driving innovation in the use of cloud and streaming technologies.


Key Responsibilities

Data Platform Development



  • Design, build, and maintain secure, cloud-native data pipelines and infrastructure


  • Apply best practices in CI/CD, infrastructure-as-code, observability, and automation


  • Develop high-performance ingestion and transformation pipelines for structured and unstructured data



Data Integration & Transformation



  • Collaborate with stakeholders across engineering, trading, and operations to define requirements


  • Develop efficient ETL/ELT flows for analytics, risk, and reporting systems


  • Lead data migration and integration initiatives across diverse platforms



Data Architecture & Governance



  • Design and maintain databases, NoSQL systems, and data warehouses


  • Implement streaming, caching, and batch systems for real-time and large-scale workloads


  • Ensure compliance with data governance, metadata management, and security standards



About You

  • 5–10 years’ experience in data engineering, ideally in fintech, trading, or other high-volume data environments


  • Strong skills in Python, Java, SQL, bash, or PowerShell, including libraries such as NumPy, Pandas, and Matplotlib


  • Solid experience with AWS (ECS, S3, Redshift, Kinesis, EMR, etc.)


  • Hands-on familiarity with Kafka, Airflow, Beam, Spark, Hadoop, Snowflake, or Databricks


  • Experience with DevOps, containerisation (Docker), CI/CD, and infrastructure automation


  • Strong analytical mindset, excellent communication, and a collaborative approach to problem-solving



What’s on Offer

  • Competitive compensation and hybrid flexibility


  • Opportunity to work on large-scale, high-performance data systems


  • Exposure to advanced cloud and event-driven architectures


  • Collaborative, global environment focused on innovation and excellence


  • Clear progression within a growing technology team



To find out more about this fantastic role and potentially be one of the first hires in NI, feel free to reach out to Ryan Quinn directly on LinkedIN. This is one of the best roles within Data Engineering, currently available in NI.


#J-18808-Ljbffr

Related Jobs

View all jobs

Principal Data Engineer/Architect

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

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 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.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.