Python Data Engineer & Data Scientist

Marylebone
3 weeks ago
Create job alert

About the Company

Our client is striving to become the top provider of data-driven marketing and analytics in the Sports Betting and iGaming sectors. They leverage deep industry knowledge, proprietary technology, and expert media execution to deliver impactful results.

Specialising in cutting-edge acquisition and retention strategies across Meta, Programmatic, PPC, and alternative traffic channels, they excel in regulated, grey, and blackhat advertising methods, particularly for crypto casinos and sportsbooks.

What We Are Recruiting For

We are seeking a Python Data Engineer & Data Scientist to drive data strategy in digital advertising and marketing. This role blends Software Engineering, Data Engineering, Data Visualisation, and Data Science, ensuring seamless data integration. You will develop and maintain data pipelines, API integrations, and processing systems, with a future focus on AI and machine learning (ML). Experience in the gambling industry is preferred, along with a proactive, business-focused mindset. Collaborating with marketing teams and BI specialists, you will deliver insights to optimise decision-making and support AI-driven solutions.

What You Will Be Doing?

  • Data Pipeline Development & Maintenance – Design and optimise ETL/ELT processes, ensuring reliable and scalable data pipelines. Integrate campaign metrics like budgets, CTRs, CPAs, and ROI.

  • API Integration – Build and maintain API connections for platforms like Facebook Ads, Google Ads, and TikTok. Integrate data with BI tools such as Looker Studio, Tableau, and Power BI.

  • AI/ML Exploration & Implementation – Research and prototype AI/ML models for campaign optimisation. Explore tools like GPT and LangChain for automation and insights.

  • System Integration & Automation – Develop automated workflows and trigger actions in advertising systems based on data insights.

  • Collaboration & Best Practices – Work with cross-functional teams, implement software engineering best practices, and ensure data privacy and security compliance.

    What You Will Bring To The Party?

  • Technical Expertise – Proficiency in Python, data processing libraries (Pandas, NumPy), and ETL/ELT pipeline architecture. Strong API integration experience and knowledge of digital marketing metrics.

  • Data & Analytics Skills – Familiarity with BI tools like Looker Studio, Tableau, and Power BI. Understanding of GCP services, data orchestration tools (Apache Beam, Airflow, Prefect), and AI/ML frameworks (PyTorch, TensorFlow, Scikit-learn).

  • AI & Automation – Interest or experience in LLMs (LangChain, GPT) and AI-driven marketing automation. Ability to build and maintain AI/ML pipelines.

  • Software Best Practices – Experience with version control (Git), CI/CD, testing, and documentation. Understanding of AI/ML deployment and monitoring.

  • Soft Skills – Strong problem-solving abilities, a proactive mindset, and excellent communication skills. Ability to collaborate with non-technical teams and work independently in a fast-paced environment.

    What You Will Get In Return?

  • 25 days paid holiday per annum

  • Enrollment into pension scheme

  • Discretionary bonus

  • Hybrid working (3 days in the office)

  • Home office equipment

  • Learning and development budget

  • Regular team socials

  • Potential enrollment into EMI Scheme for employee share options

Related Jobs

View all jobs

Python Engineer / Data Scientist

Data Engineer

Data Engineer - Python & Azure

Senior Data Engineer

Lead Data Engineer

Sustainability Data Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

UK Visa & Work Permits Explained: Your Essential Guide for International Data Engineering Talent

Data engineering has become a foundational pillar for organisations seeking to leverage their data assets effectively. From building robust data pipelines and integrating real-time analytics to migrating entire infrastructures to the cloud, skilled data engineers drive innovation and growth. In the United Kingdom, demand for data engineering professionals spans multiple sectors, including finance, healthcare, retail, tech start-ups, and government services. However, if you’re an international data engineering specialist looking to build or advance your career in the UK, one critical step stands before you: navigating the visa and work permit landscape. This comprehensive guide breaks down key visa routes, eligibility criteria, and practical steps to help you secure employment and settle into the UK’s thriving data ecosystem. Whether you specialise in ETL processes, big data platforms, or cloud infrastructure, understanding the UK visa system is the first step toward realising your ambitions.

Top UK Data Engineering Labs and Institutes: Powering the Future of Data-Driven Innovation

Data is the new “oil” of the modern digital economy, but it only becomes valuable when refined into usable insights. That’s where data engineering steps in—building the pipelines, architectures, and systems that underpin data-intensive applications across every sector. From healthcare analytics to streaming media platforms, from cutting-edge AI research to real-time financial trading, the demand for robust data infrastructure is constantly growing. In the United Kingdom, data engineering has emerged as a fast-expanding field, supported by world-class universities, cutting-edge research institutes, and a thriving tech industry. In this guide—brought to you by DataEngineeringJobs.co.uk—we explore the top UK labs and institutes driving data engineering innovation, examine the country’s data-focused ecosystem, and outline the career paths that can lead you to success in this vital discipline.

Shadowing and Mentorship in Data Engineering: Gaining Experience Before Your First Full-Time Role

Introduction Data engineering has emerged as a critical and fast-growing field in today’s business landscape. With organisations across every industry striving to collect, organise, and derive insights from massive volumes of data, the demand for talented data engineers continues to surge. These professionals design and maintain the frameworks that power data pipelines, ensuring that large datasets can be processed efficiently, securely, and accurately to support analytics and machine learning initiatives. Despite the expanding opportunities, breaking into data engineering can be challenging—particularly if you lack hands-on experience with real-world systems. Even those who have completed relevant degree programmes, bootcamps, or online courses often find themselves seeking more practical knowledge and industry exposure. That’s where shadowing and mentorship become pivotal. Observing seasoned data engineers at work and receiving guidance from mentors can fast-track your learning, helping you bridge the gap between theoretical knowledge and the daily realities of data infrastructure. This article explores how to identify mentors, build productive mentor-mentee relationships, and engage in shadowing opportunities that deepen your technical and professional skills. From landing an internship to leveraging online communities, we’ll cover actionable strategies to strengthen your profile and confidently pursue your first full-time role in data engineering.