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

Apply Now

Data Engineer

Rapid7
Belfast
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer II


Rapid7 seeks a highly motivated and inquisitive aspiring Data Engineer II to join our quickly scaling data engineering function. Come and join our efforts in unlocking the value of data through industry-leading innovation, cutting edge modern tooling, democratization at scale and building exceptional and trusted data products for the company! 

About the Team


As we spearhead a cultural shift to a data-driven business, Data Engineering serves as the Hub for all teams at Rapid7 from ML Ops, to Sales and Operations to Platform and Engineering. Our team is a highly skilled yet egoless group of data magicians (and humorists) with a penchant for innovation and a knack for problem solving. 

About the Role


The Data Engineering practice is growing quickly and we’re investing in a bright, data-focused future. We are seeking an aspiring data engineer to flourish and grow within our team. The ideal candidate has a solid foundational understanding of data engineering and software development concepts and best practices with some hands on experience preferred. Bring your courage, curiosity, problem solving skills, and technical chops!

In this role, you will:

Build and maintain pipelines and infrastructure that ingest, analyze and store Rapid7's enterprise data using modern tools such as Snowflake, Airflow, dbt and AWS

Work closely with senior engineers to drive software lifecycle including hands-on development, testing, deployment, and documentation

Participate in scrum events include sprint planning, retrospectives and daily stand-ups

Productionize data through dev ops processes (such as CICD) using containerization tools such as ECS

Collaborate with stakeholders in product, business and IT to deliver high quality data products and assist with data-related technical issues

Support large scale projects including major implementations, process improvements, and cross-function data initiatives 

The skills you’ll bring include:

BS in Computer Science, Analytics, Statistics, Informatics, Information Systems or 

another quantitative field or equivalent experience; Should have broad knowledge of core computer science / software engineering concepts.

2-3 years of experience in a data-focused required; specifically as a Data Engineer or highly technical Analytics Engineer

SQL fluency and data warehousing understanding required; Working experience with a programming language is highly preferred

Working knowledge with modern data tools such as Snowflake, dbt, Airflow, and AWS

Capable of taking well-defined tasks and completing these tasks with minimal supervision

General understanding of the SDLC including modern dev ops tools, code reviews, testing, and planning

Strong work ethic, resiliency, persistence, and urgency; Data Engineering holds itself to a high standard so you’ll need to keep up!

Sharp business and interpersonal skills; Should be able to effectively communicate status and escalate blockers

Be a team-player! Data Engineering has a nice balance of independent vs codependent - One Moose!



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.

 

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.