Senior Data Engineer - Quantexa at KPMG, London/Remote, 12 Months, £Contract Rate

(ALL KPMG UK CONTRACTORS MUST RESIDE AND HAVE PROOF OF RIGHTS TO WORK IN THE UK) 

At KPMG, our Data & AI practice is a dynamic and rapidly growing capability, delivering data-driven solutions that generate measurable impact for our clients. As part of this practice, Quantexa plays a key role in helping organisations unlock insights through entity resolution, network analytics, and contextual decision intelligence.

We are seeking a Senior Data Engineer to join our Quantexa delivery team. This role offers the opportunity to work on large-scale data engineering solutions using the Quantexa platform, contribute to architecture design, and lead development efforts within cross-functional agile teams.

The Role:

As a Senior Data Engineer, you will lead the technical development of Quantexa-based solutions, bringing deep hands-on experience with data ingestion, transformation, and contextual data modelling. You will collaborate closely with Quantexa Architects, Tech Leads, and client stakeholders to deliver high-quality solutions that address complex business challenges.

You will play a critical role in designing and implementing robust data pipelines, configuring Quantexa components, and supporting the overall success of client engagements.

Why Join Us?

As a Senior Data Engineer in our Quantexa team, you will be at the forefront of delivering impactful data solutions that transform how organisations understand and act on their data. You’ll join a supportive, collaborative environment where innovation is encouraged, continuous learning is expected, and your contributions make a real difference. This is an opportunity to work on high-profile projects, grow your technical skills, and take the next step in your career within a leading Data & AI practice.

Key Responsibilities: 

• Develop and optimise data ingestion pipelines and transformations within the Quantexa platform using Spark and Scala
• Configure and implement Quantexa components such as Entity Resolution, Scoring, and Network Generation to support specific use cases
• Collaborate with Tech Leads and Solution Architects to design scalable and performant Quantexa solutions
• Translate business and technical requirements into efficient, production-ready data engineering solutions
• Support the integration of Quantexa into broader enterprise data architectures, working closely with cloud, security, and DevOps teams
• Conduct peer code reviews and ensure adherence to coding standards and best practices
• Mentor junior engineers and contribute to upskilling the wider team on Quantexa best practices
• Actively participate in sprint planning, backlog refinement, and agile ceremonies across delivery pods
• Contribute to client-facing workshops and documentation, including design specifications, deployment guides, and runbooks
• Stay current on developments in Quantexa and adjacent Big Data technologies to bring innovative ideas to the team

Required Skills & Experience:

• Quantexa Technical Certification is required
• 6+ years of experience in data engineering roles, with at least 2 years of hands-on experience implementing Quantexa solutions
• Strong proficiency in Scala, Apache Spark, and Big Data frameworks (e.g., Hadoop, HDFS)
• Demonstrated experience with core Quantexa components:
• Data Ingestion Pipelines
• Entity Resolution (ER) Configuration
• Scoring Logic Implementation
• Contextual Network Generation
• Strong understanding of distributed systems, data modelling, and performance tuning techniques
• Ability to write clean, maintainable, and production-grade code following software engineering best practices
• Excellent communication and interpersonal skills, with experience working in cross-functional agile teams
• Familiarity with cloud platforms (AWS, Azure, or GCP), especially in relation to data services and infrastructure
• Experience with version control (Git), CI/CD tools, and build automation

Preferred Qualifications:

• Experience delivering Quantexa in Financial Services, Fraud Detection, AML, or KYC domains
• Exposure to DevOps and CI/CD pipelines, including tools such as Jenkins, GitHub Actions, or Azure DevOps
• Familiarity with containerisation technologies like Docker and Kubernetes.
• Understanding of data governance, data quality frameworks, and enterprise data security standards
• Bachelor’s or master’s degree in computer science, Data Engineering, or related technical field