Data Engineer - active NPPV3 clearance required

Farringdon
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer - AI Analytics and EdTech Developments

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineering & Data Science Consultant

Data Engineering & Data Science Consultant

MLOps Data Engineer (GCP)

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.

Careers

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

Jobs

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

Education

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.