Software Engineer in Slough

Software Engineer in Slough

Slough Full-Time 60000 - 80000 £ / year (est.) Working from home possible
Navigator

At a Glance

  • Tasks: Build and scale systems for advertising and data workflows using cutting-edge tech.
  • Company: Join a dynamic team focused on innovative solutions in the tech industry.
  • Benefits: Enjoy competitive salary, remote work options, and opportunities for professional growth.
  • Other info: Great chance to grow across multiple engineering domains in a supportive environment.
  • Why this job: Make a real impact by working on challenging distributed systems and large-scale data workflows.
  • Qualifications: 3-5 years in software or data engineering with strong Python and SQL skills.

The predicted salary is between 60000 - 80000 £ per year.

We’re looking for a Software Engineer to build and scale the systems powering our advertising and data workflows. We work with large-scale advertising and attribution datasets, building systems that connect audience intelligence with campaign execution and measurement. This role sits across backend engineering, data engineering, and ML/data infrastructure. You’ll work across APIs, event pipelines, data infrastructure, partner integrations, and internal tooling supporting campaign workflows and ML/data systems. The role is best suited for engineers who enjoy operating across systems rather than staying within a single specialization.

What You’ll Work On

  • Backend & Platform Engineering
    • Build and maintain backend services, APIs, and internal tooling
    • Design asynchronous workflows and distributed processing systems
    • Improve observability, reliability, and deployment workflows
    • Debug production issues across infrastructure, application, and data layers
  • Data Engineering
    • Build and maintain scalable ingestion and processing pipelines
    • Process high-volume event and attribution datasets across operational systems
    • Design reliable data workflows and maintainable data models
  • ML Infrastructure & Experimentation
    • Support ML systems with reliable training and inference datasets
    • Build pipelines supporting experimentation and feature generation
    • Collaborate with ML engineers on production integrations and evaluation workflows
  • Partner Data & Integrations
    • Integrate external APIs, partner platforms, and operational systems
    • Build resilient ingestion systems handling retries, quotas, pagination, and evolving schemas
    • Support privacy-conscious attribution and partner data workflows

Tech Stack

  • Core Technologies
    • Python
    • SQL
    • PostgreSQL
    • Snowflake
  • Infrastructure & Platform
    • GCP or AWS
    • CI/CD pipelines
    • Infrastructure as code
  • Nice to Have
    • dbt
    • FastAPI or similar backend frameworks
    • Kafka, Pub/Sub, or streaming systems
    • AdTech, MarTech, or travel-tech systems
    • ML data or experimentation systems

What We’re Looking For

  • 3–5 years of experience in software engineering, data engineering, or platform engineering
  • Strong programming skills, especially in Python
  • Solid SQL and data modeling fundamentals
  • Experience building production systems in cloud environments
  • Understanding of distributed systems, asynchronous workflows, and operational reliability
  • Strong ownership mindset and ability to drive work independently
  • Strong engineering fundamentals and ability to quickly learn unfamiliar systems, tools, and domains

Nice-to-Have Experience

  • High-volume event or analytics systems
  • Advertising, attribution, or customer data platforms
  • ML data pipelines or experimentation systems
  • API-heavy integration platforms
  • Comfortable using AI-assisted engineering tools such as Claude Code, Gemini CLI, OpenAI Codex, GitHub Copilot, or similar developer agents as part of day-to-day development workflows

Why Join

  • Work on challenging distributed systems and large-scale data workflows
  • Build modern platform and data infrastructure with real business impact
  • High ownership and opportunity to grow across multiple engineering domains

Software Engineer in Slough employer: Navigator

Join a forward-thinking company that values innovation and collaboration, offering a dynamic work culture where engineers thrive on tackling complex challenges in distributed systems and large-scale data workflows. With a strong emphasis on employee growth, you will have the opportunity to expand your skills across various engineering domains while making a tangible impact on business outcomes. Enjoy the flexibility of remote or hybrid work arrangements, alongside a supportive environment that encourages ownership and creativity.

Navigator

Contact Details:

Navigator Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Software Engineer in Slough

Tip Number 1

Network like a pro! Reach out to folks in your industry on LinkedIn or at meetups. A friendly chat can lead to opportunities that aren’t even advertised yet.

Tip Number 2

Show off your skills! Create a portfolio or GitHub repo showcasing your projects, especially those related to backend services or data workflows. It’s a great way to demonstrate what you can do beyond your CV.

Tip Number 3

Prepare for the technical interview! Brush up on your Python and SQL skills, and be ready to discuss distributed systems and asynchronous workflows. Practice coding challenges to get in the zone.

Tip Number 4

Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Plus, it makes tracking your application easier for both of us!

We think you need these skills to ace Software Engineer in Slough

Python
SQL
PostgreSQL
Snowflake
GCP or AWS
CI/CD pipelines
Infrastructure as code

Some tips for your application 🫡

Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with Python, SQL, and any relevant cloud environments to show us you’re the right fit for our Software Engineer role.

Craft a Compelling Cover Letter:Use your cover letter to tell us why you’re passionate about working with large-scale data workflows and distributed systems. Share specific examples of your past projects that align with what we do at StudySmarter.

Showcase Your Problem-Solving Skills:In your application, don’t just list your technical skills; demonstrate how you’ve used them to solve real-world problems. We love engineers who can think critically and tackle challenges head-on!

Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining the StudySmarter team!

How to prepare for a job interview at Navigator

Know Your Tech Stack

Make sure you’re well-versed in the core technologies mentioned in the job description, especially Python and SQL. Brush up on your knowledge of cloud environments like GCP or AWS, as well as any relevant frameworks like FastAPI. Being able to discuss your experience with these tools will show that you’re ready to hit the ground running.

Showcase Your Problem-Solving Skills

Prepare to discuss specific challenges you've faced in previous roles, particularly around distributed systems and data workflows. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how you approached problems and what impact your solutions had.

Demonstrate Your Ownership Mindset

This role values a strong ownership mindset, so be ready to share examples of how you've taken initiative in past projects. Talk about times when you drove work independently, made decisions, and how those decisions benefited your team or project outcomes.

Ask Insightful Questions

Prepare thoughtful questions about the company’s approach to ML infrastructure and data workflows. This not only shows your interest in the role but also gives you a chance to assess if the company aligns with your career goals. Consider asking about their current challenges in scaling systems or how they integrate partner data.