Data Engineer - active NPPV3 clearance required

Farringdon
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

PLEASE NOTE - That to be considered you must be in possession of active NPPV3 clearance.

THE ROLE

  • To design, implement, and develop robust and scalable data infrastructure that supports advanced analytics and intelligence operations within the police department, enabling data-driven decision-making for crime prevention, investigations, and public safety.

  • This post will work within a 130-strong team of intelligence professionals.

  • Enabling seamless integration and analysis of complex criminological and intelligence data, empowering analysts and investigators to identify crime patterns, predict future incidents, and enhance investigative outcomes.

  • Ensuring the integrity, security, and ethical use of sensitive criminal justice information, adhering to stringent compliance standards and fostering public trust.

  • Drive innovation in data management and analytics, leveraging cutting-edge technologies to enhance the department's ability to respond to evolving crime trends and emerging threats.

  • Empower the department with the tools to transform data into actionable intelligence.

    PRIME RESPONSIBILITIES

  • Design and implement data architectures and data models. This involves creating blueprints for how data is organized, stored, and accessed. It includes defining data schemas, relationships, and flows, ensuring data consistency and efficiency.

  • Build data pipelines to process and analyse intelligence data from various sources to identify relevant threats.

  • Develop data solutions to support the analysis of complex intelligence networks and identify potential criminal activity.

  • Administer and maintain databases, ensuring data availability, integrity, and security. It also involves designing and implementing data warehouses to support analytical reporting and data mining. Implement and enforce data security and compliance measures.

  • Collaborate closely with stakeholders to understand their data requirements and develop customized data solutions.

  • Optimize data infrastructure performance and troubleshoot issues by monitoring system performance, identifying bottlenecks, and implementing solutions to improve efficiency. It also includes diagnosing and resolving technical problems.

  • Manage cloud-based data infrastructure, optimise cost, performance, and scalability.

  • Establish and enforce data governance and quality standards by defining and implementing policies and procedures to ensure data accuracy, consistency, and completeness. It also includes establishing data lineage and metadata management processes.

  • Participate in the development of data strategies and initiatives, identifying opportunities to leverage new technologies, and driving innovation in data management practices.

  • Work closely with data scientists, intelligence analysts, and other stakeholders to understand their data needs and provide effective solutions. It also involves communicating complex technical concepts clearly and concisely.

    SKILLS ATTRIBUTES

  • Proficiency in advanced programming languages used for data engineering tasks, including data manipulation, transformation, and analysis (Python, SQL, etc.).

  • Experience with tools and technologies used to build and manage data pipelines, including message queues, orchestration tools, and data integration platforms (Kafka, Airflow, etc.).

  • Familiarity with cloud-based data services, including storage, compute, and analytics (AWS, Azure).

  • Knowledge of database management systems (relational and NoSQL) and data warehousing concepts and technologies.

  • Understanding of data security principles and compliance requirements, particularly related to sensitive data.

  • Ability to support team members, share knowledge, and foster their professional development.

  • Ability to identify and resolve complex technical problems and analyse data to identify trends and patterns.

  • Ability to communicate technical concepts clearly and concisely and work effectively with stakeholders from diverse backgrounds.

    Mobile Site Contact Us About Partners Terms Privacy Cookies

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

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.

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.