Lead Big Data Software Engineer

Rapid7
Belfast
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

About the Team

The Rapid7 Data Platform is a unified, integrated platform powered by Rapid7’s product suite providing our customers enhanced visibility into their attack surface, operational efficiency, risk management, and decision-making capabilities. 

Our teams are responsible for consolidating data from all Rapid7 products, transforming it for optimised retrieval, and ensuring high-performance and seamless access to our customers. This role is crucial to the platform’s success as it focuses on building a highly scalable and reliable data mesh that powers cross-product use cases through a distributed query engine for big data analytics.

About the Role

We are seeking an innovative, self-motivated Data and Performance Engineer who will act as a technical leader to collaborate with our product teams to optimise their data pipelines and retrieval processes for performance and efficiency. You will work with the Data Platform teams to implement monitoring and testing strategies to ensure the performance of the data and their queries as well as identify optimisations.

Technologies you will work with:

Trino

Iceberg

Parquet

Spark

Airflow

Kafka

AWS services such as Glue, S3, EKS

In this role, you will:

Analyse and optimise distributed SQL queries to improve performance

Suggest optimisations to our data pipelines

Provide recommendations for efficient partitioning strategies and schema designs

Conduct performance tuning for the data pipelines and queries

Develop performance monitoring strategies and tools

The skills you'll bring include:

5+ years of hands-on software engineering experience, with a specific focus on database query optimization

Strong database system expertise in query execution planning, query optimization, performance tuning, parallel computing, and schema design

Experience in continuously monitoring and optimising data pipelines for performance and cost-effectiveness

Ability to design, develop, implement, and operate highly reliable large-scale data lake systems in cooperation with product teams

Skills to analyse and performance test the data mesh performance and scalability, identify bottlenecks, recommend and develop improvements

Mentorship and guidance of junior engineers, providing technical leadership and fostering a culture of continuous improvement and innovation

Excellent verbal and written communication skills.

Strong, creative problem solving ability.

Nice to haves:

Trino/Presto data-mesh

AWS, Terraform, Kubernetes

Java

Kafka

We believe the best ideas and solutions come from diverse teams. If you're excited about this role and feel your experience can make an impact, don't hesitate – apply today!

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 Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.