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

Apply Now

Data Engineer

Seargin
Glasgow
4 days ago
Create job alert

Employment Type: engagement is inside IR‑35 through an umbrella company

Requirements 'must have':

  • Education: Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
  • 4+ years of experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc.
  • 3+ years hands-on experience with cloud services, especially Databricks, for building and managing scalable data pipelines
  • 3+ years of proficiency in working with Snowflake or similar cloud-based data warehousing solutions
  • 3+ years of experience in data development and solutions in highly complex data environments with large data volumes.
  • Solid understanding of ETL principles, data modelling, data warehousing concepts, and data integration best practices-Familiarity with agile methodologies and the ability to work collaboratively in a fast-paced, dynamic environment.
  • Experience with code versioning tools (e.g., Git)
  • Knowledge of Linux operating systems
  • Familiarity with REST APIs and integration techniques
  • Familiarity with data visualization tools and libraries (e.g., Power BI)
  • Background in database administration or performance tuning
  • Familiarity with data orchestration tools, such as Apache Airflow
  • Previous exposure to big data technologies (e.g., Hadoop, Spark) for large data processing
  • Strong analytical skills, including a thorough understanding of how to interpret customer business requirements and translate them into technical designs and solutions.
  • Strong communication skills both verbal and written. Capable of collaborating effectively across a variety of IT and Business groups, across regions, roles and able to interact effectively with all levels.
  • Self-starter. Proven ability to manage multiple, concurrent projects with minimal supervision. Can manage a complex ever changing priority list and resolve conflicts to competing priorities.
  • Strong problem-solving skills. Ability to identify where focus is needed and bring clarity to business objectives, requirements, and priorities.


Requirements 'nice to have':

  • Experience in financial services
  • Knowledge of regulatory requirements in the financial industry


Tasks:

  • Collaborating with cross-functional teams to understand data requirements, and design efficient, scalable, and reliable ETL processes using Python and Databricks
  • Developing and deploying ETL jobs that extract data from various sources, transforming them to meet business needs.
  • Taking ownership of the end-to-end engineering lifecycle, including data extraction, cleansing, transformation, and loading, ensuring accuracy and consistency.
  • Creating and managing data pipelines, ensuring proper error handling, monitoring and performance optimizations
  • Working in an agile environment, participating in sprint planning, daily stand-ups, and retrospectives.
  • Conducting code reviews, providing constructive feedback, and enforcing coding standards to maintain a high quality.
  • Developing and maintaining tooling and automation scripts to streamline repetitive tasks.
  • Implementing unit, integration, and other testing methodologies to ensure the reliability of the ETL processes
  • Utilizing REST APIs and other integration techniques to connect various data sources
  • Maintaining documentation, including data flow diagrams, technical specifications, and processes.
  • Designing and implementing tailored data solutions to meet customer needs and use cases, spanning from streaming to data lakes, analytics, and beyond within a dynamically evolving technical stack.
  • Collaborate seamlessly across diverse technical stacks, including Databricks, Snowflake, etc.
  • Developing various components in Python as part of a unified data pipeline framework.
  • Contributing towards the establishment of best practices for the optimal and efficient usage of data across various on-prem and cloud platforms.
  • Assisting with the testing and deployment of our data pipeline framework utilizing standard testing frameworks and CI/CD tooling.
  • Monitoring the performance of queries and data loads and perform tuning as necessary.
  • Providing assistance and guidance during QA & UAT phases to quickly confirm the validity of potential issues and to determine the root cause and best resolution of verified issues.
  • Adhere to Agile practices throughout the solution development process.
  • Design, build, and deploy databases and data stores to support organizational requirements.

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

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.