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

Apply Now

Lead Data Engineer - (MongoDB and Kafka)

Develop
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer (MongoDB, Kafka, Java)Salary: Competitive plus generous benefits package Location: Hybrid working with occasional travel to key sites across the UK (London, Bristol, Gloucester, Edinburgh)About the Role A leading financial services organization is undergoing a major digital transformation, placing technology at the heart of its growth strategy. They are investing in an enhanced in-house capability to reshape the future of banking. This transformation presents a unique opportunity for a forward-thinking IT professional to play a critical role in building next-generation data-driven systems.How You'll Make a Difference The Lead Data Engineer will be a central figure in designing, developing, and deploying mission-critical data applications and systems. Combining deep technical expertise with leadership skills, the role involves driving innovation, managing complex projects, and mentoring junior engineers. The successful candidate will set the technical direction for projects, ensuring that solutions align with business goals while maintaining the highest quality standards.Key ResponsibilitiesLead the design and implementation of data-driven solutions across the enterprise.Collaborate with cross-functional teams to align technology strategy with business objectives.Mentor and support junior engineers, fostering a culture of continuous learning.Ensure data systems are scalable, maintainable, and secure.Drive improvements in processes, technologies, and systems architecture. What You'll Bring Essential Skills & Experience:Bachelor's or Master's degree in Computer Science, Engineering, or equivalent experience.Expertise in agile application development using Java and microservices, with a focus on MongoDB and Kafka.Strong proficiency in data architecture, design patterns, and best practices.Experience with CI/CD pipelines and version control systems like Git.Proven ability to design scalable, real-time data applications. Technical Expertise: Must have modern, recent experience with MongoDB and Kafka in a data engineering capacityReal-Time Data ApplicationsData Management: MongoDB, Cassandra/ScyllaDBData Integration: Kafka, Kafka Streams, Java, APIs (GraphQL)Data Analytics Applications:Data Management: Teradata, Azure Data Lake, Snowflake, DatabricksData Modelling: Dimensional Modelling, Kimball DesignData Integration: IBM DataStage, SQL, Azure Data Factory, AWS GlueBatch Orchestration: TWS/OPC, JCLData Visualization: Power BIData Analytics: SAS or comparable tools What's in It for YouHybrid & Flexible Working: Supporting a healthy work/life balance.Reward & Benefits Package: A personalized benefits program.Dynamic Work Environment: A collaborative and inclusive culture.Career Growth: Opportunities for development and progression

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.