National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Principal Software Engineer - Data Platform

Rapid7 International Limited
Belfast
8 months ago
Applications closed

Related Jobs

View all jobs

Principal Software Developer

Head of Engineering

Principal Data Engineer

Senior Data Engineer

Data Engineering Lead

Data Engineering Lead

As a Principal Engineer, you’ll get the opportunity to be a hands-on engineer, learning best practice engineering processes and approaches whilst receiving ongoing development through coaching, mentoring and pairing with other engineers on your team. From problem-solving to challenging old ways of thinking, you will have the opportunity to unleash your full potential and creativity whilst working with cutting edge technologies in a dynamic and collaborative team. About the Team The Data Platform team is responsible for building ETL Pipelines that fuel the Data Platform at Rapid7. Moving Product Data into our Data Platform for product teams to develop new features, enhance existing features and build shared experiences to create value for customers across the world. We have a cutting edge data stack including Kafka, K8s, Spark and Iceberg. About the Role The Principal Engineer role is a part of our Data Platform Engineering team. In this role you will be focussed on helping our product teams move data into our Data Platform for in product experiences and product analytics. As a Principal Engineer on the Data Platform Engineering team, you will be responsible for architecting and scaling streaming and batch data pipelines, while also designing the CI/CD infrastructure that ensures efficient development and deployment of data services. You will play a key role in shaping the architecture of our data platform, collaborating with cross-functional teams to deliver highly available, performant, and scalable solutions for both real-time and large-scale data processing. In this role, you will: Architect and implement a highly scalable Data Platform that supports Change Data Capture (CDC) using Debezium and Kafka for data replication across different databases and services. Design and maintain large-scale data lakes using Apache Iceberg, ensuring efficient data partitioning, versioning, and schema evolution to support real-time analytics and historical data access. Build and optimize CI/CD pipelines for the deployment and automation of data platform services using tools like Jenkins. Lead the integration of Apache Spark for large-scale data processing and ensure that both batch and streaming workloads are handled efficiently. Collaborate with our Platform Delivery teams to ensure high availability and performance of the data platform, implementing monitoring, disaster recovery, and automated testing frameworks. Provide technical leadership and mentoring to junior engineers, promoting best practices in CDC architecture, distributed systems, and CI/CD automation. Ensure that the platform adheres to data governance principles, including data lineage tracking, auditing, and compliance with regulatory requirements. Stay informed about the latest advancements in CDC, data engineering, and infrastructure automation to guide future platform improvements. Work closely with product and data science teams to understand business requirements and translate them into scalable and efficient data platform solutions. Stay current with the latest trends in data engineering and infrastructure, making recommendations for improvements and introducing new technologies as appropriate. The skills you’ll bring include: 10 years of experience in software engineering with a focus on data platform engineering, data infrastructure, or distributed systems. Expertise in building data pipelines using Apache Kafka or similar for ingesting, processing, and distributing high-throughput data. Strong experience designing and managing CI/CD pipelines for data platform services using tools such as Jenkins. Experience with Apache Iceberg (or similar Delta Lake/Apache Hudi) for managing versioned, partitioned datasets in data lakes with an understanding of Apache Spark for both batch and streaming data processing, including optimization strategies for distributed data workloads. Expertise in designing distributed systems and managing high-throughput, fault-tolerant, and low-latency data architectures. Strong programming skills in Java, Scala, or Python. Experience with cloud-based environments (AWS, GCP, Azure) and containerized infrastructure using Kubernetes and Docker. The attitude and ability to thrive in a high-growth, evolving environment Collaborative team player who has the ability to partner with others and drive toward solutions Strong creative problem solving skills Solid communicator with excellent written and verbal communications skills both within the team and cross functionally Passionate about delighting customers, puts the customer needs at the forefront of all decision making Excellent attention to detail We know that the best ideas and solutions come from multi-dimensional teams. That’s because these teams reflect a variety of backgrounds and professional experiences. If you are excited about this role and feel your experience can make an impact, please don’t be shy - apply today. LI_FB1

National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.