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

Apply Now

Data Engineer

PortSwigger Ltd
Knutsford
1 week ago
Create job alert
Overview

The Opportunity: Build and Own Our Data Future

At our core, we’re a data-driven SaaS company. But to get to the next level, we need to evolve. We're looking for an experienced Data Engineer who has scaled a company's data maturity before and is ready to do it again.

This isn't a role for maintaining legacy systems. This is a unique opportunity to take the lead on a significant data re-architecture project. You will have the autonomy and trust to make critical architectural decisions, laying the technical foundation that will empower our entire business—from product and analytics to customer intelligence and growth. If you are motivated by high-impact work and the challenge of building a best-in-class, scalable data platform from the ground up, we want to talk to you.

This Role is for You If…

Your Experience & Technical Craft:

  • You have 4-6+ years of professional experience as a Senior or Lead Data Engineer, defined by successfully leading at least one significant data re-architecture project.
  • You possess deep expertise in SQL and Python and apply data engineering best practices as second nature (testing, version control, CI/CD).
  • You have strong, hands-on experience building scalable data pipelines in a modern cloud environment, using tools like dbt, AWS Glue, AWS Lake Formation, Apache Spark, and Amazon Redshift.
  • You have a firm grasp of data modeling, ELT design patterns, data governance, and security best practices.
Your Approach to Work
  • You are driven by autonomy and thrive when given the freedom to solve complex, ambiguous problems. You are frustrated by inefficiency and micromanagement.
  • You are a natural communicator who builds strong relationships, consults with stakeholders, and ensures everyone is aligned before moving forward.
  • You have a hybrid work style: highly collaborative when framing a problem, but disciplined and independent when building the solution.
  • You are genuinely geeky about data, best practices, and new tooling. You are described by others as solution-oriented, proactive, and approachable.
  • You see constructive feedback as a vital opportunity for growth.
What Success Looks Like
  • Our data pipelines are highly expandable and reliable, enabling the efficient development of new data products.
  • Teams across the company can easily access accurate, trustworthy data to make better decisions and drive growth.
  • Data is well-documented, discoverable, and monitored, reducing duplication and confusion.
  • You’ve become a trusted partner to both technical and non-technical teams, helping them unlock value from data.
Bonus Points If You Have…
  • Exposure to reverse ETL tools like Census.
  • Knowledge of data privacy regulations (e.g., GDPR, ISO 27001).
  • Experience with customer-facing analytics features in a multi-tenant SaaS product.
  • Experience building data pipelines to support AI and machine learning use cases.
Why Join PortSwigger?

We’re a team of curious, driven people working together to secure the web. Our culture is our superpower—collaborative, human, and focused on meaningful work. Read more about our culture and values on our Careers page.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

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.