National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Engineer

SearchWorks
London
2 days ago
Create job alert

Company
My client are a fast-growing startup revolutionising the hospitality industry by connecting businesses with a flexible, student-powered workforce. The platform enables partners—from local pubs to multinational chains—to optimise staffing in real-time, enhancing efficiency and profitability.
The Role
As a Data Engineer, you'll play a pivotal role in shaping the data infrastructure. Your responsibilities will include:
Building and Maintaining Data Pipelines: Design and implement scalable ETL processes to integrate data into our mobile and web applications, as well as internal dashboards.
Data Architecture Design: Develop and optimise data storage, retrieval, and processing systems to ensure efficiency and scalability.
Collaboration: Work closely with data scientists, software engineers, and internal stakeholders to understand data needs and deliver solutions that drive data-driven decisions.
Monitoring and Troubleshooting: Oversee pipeline performance, address issues promptly, and maintain comprehensive data documentation.
What You’ll Bring
Technical Expertise: Proficiency in Python and SQL; experience with data processing frameworks such as Airflow, Spark, or TensorFlow.
Data Engineering Fundamentals: Strong understanding of data architecture, data modelling, and scalable data solutions.
Backend Development: Willingness to develop proficiency in backend technologies (e.g., Python with Django) to support data pipeline integrations.
Cloud Platforms: Familiarity with AWS or Azure, including services like Apache Airflow, Terraform, or SageMaker.
Data Quality Management: Experience with data versioning and quality assurance practices.
Automation and CI/CD: Knowledge of build and deployment automation processes.
Nice to Have
Production Experience: Experience building and maintaining data pipelines in a live environment.
Data Storage Solutions: Familiarity with data lakes, warehousing, and other data storage patterns.
Advanced Tools: Experience with tools like Kafka, Jenkins, Athena, or Spark.

This role will require 4 days per week onsite in the office, this is not optional and you must be open to this in order to proceed with the role.

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

National AI Awards 2025

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.

LinkedIn Profile Checklist for Data Science Jobs: 10 Tweaks to Elevate Recruiter Engagement

Data science recruiters often sift through dozens of profiles to find candidates skilled in Python, machine learning, statistical modelling and data visualisation—sometimes before roles even open. A generic LinkedIn profile won’t suffice in this data-driven era. This step-by-step LinkedIn for data science jobs checklist outlines ten targeted tweaks to elevate recruiter engagement. Whether you’re an aspiring junior data scientist, a specialist in MLOps, or a seasoned analytics leader, these optimisations will sharpen your profile’s search relevance and demonstrate your analytical impact.

Part-Time Study Routes That Lead to Data Science Jobs: Evening Courses, Bootcamps & Online Masters

Data science sits at the intersection of statistics, programming and domain expertise—unearthing insights that drive business decisions, product innovation and research breakthroughs. In the UK, organisations from fintech and healthcare to retail and public sector are investing heavily in data-driven strategies, fuelling unprecedented demand for data scientists, machine learning engineers and analytics consultants. According to recent projections, data science roles will grow by over 40% in the next five years, offering lucrative salaries and varied career paths. Yet many professionals hesitate to leave their current jobs or pause personal commitments for full-time study. The good news? A vibrant ecosystem of part-time learning routes—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn data science while working. This comprehensive guide explores every pathway: foundational CPD units and short courses, hands-on bootcamps, accredited online MScs, plus funding options, planning strategies and a real-world case study. Whether you’re an analyst looking to formalise your skills, a software developer pivoting into data or a manager seeking to harness data-driven decision-making, you’ll find the right route to fit your schedule, budget and career goals.

The Ultimate Assessment-Centre Survival Guide for Data Science Jobs in the UK

Assessment centres for data science positions in the UK are designed to replicate the multifaceted challenges of real-world analytics teams. Employers combine psychometric assessments, coding tests, statistical reasoning exercises, group case studies and behavioural interviews to see how you interpret data, build models, communicate insights and collaborate under pressure. Whether you’re specialising in predictive modelling, NLP or computer vision, this guide provides a step-by-step roadmap to excel at every stage and secure your next data science role.