Senior Data Engineer

London
6 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer / Technical Lead
Hybrid - London with 2/3 days WFH
Circ £85,000 - £95,000 + Bonus

An excellent opportunity is available for a Senior Data Engineer / Technical Lead looking to move into a Management / Leadership position. This is an exciting newly created hands-on Data Engineer Managers post that will be responsible for leading and mentoring a small team of Data Engineers whilst overseeing data platform development and optimisation. Our client is a well-established and rapidly growing global ecommerce business with its headquarters based in London. Having implemented a new MS Fabric based Data platform, the need is now to scale up to meet the demand to deliver data driven insights and strategies right across the business globally. The role will require someone that's happy to be hands-on (potentially up to 50% of the time) as you'll be troubleshooting, doing code reviews, steering the team through deployments, acting as an escalation point for technical issues etc.

Key Responsibilities include;

  • Define and take ownership of the roadmap for the ongoing development and enhancement of the Data Platform.

  • Design, implement, and oversee scalable data pipelines and ETL/ELT processes within MS Fabric, leveraging expertise in Azure Data Factory, Databricks, and other Azure services.

  • Advocate for engineering best practices and ensure long-term sustainability of systems.

  • Integrate principles of data quality, observability, and governance throughout all processes.

  • Participate in recruiting, mentoring, and developing a high-performing data organization.

  • Demonstrate pragmatic leadership by aligning multiple product workstreams to achieve a unified, robust, and trustworthy data platform that supports production services such as dashboards, new product launches, analytics, and data science initiatives.

  • Develop and maintain comprehensive data models, data lakes, and data warehouses (e.g., utilizing Azure Synapse).

  • Collaborate with data analysts, Analytics Engineers, and various stakeholders to fulfil business requirements.

    Key Experience, Skills and Knowledge:

  • Experience leading data or platform teams in a production environment as a Senior Data Engineer, Tech Lead, Data Engineering Manager etc.

  • Proven success with modern data infrastructure: distributed systems, batch and streaming pipelines

  • Hands-on knowledge of tools such as Apache Spark, Kafka, Databricks, DBT or similar

  • Experience building, defining, and owning data models, data lakes, and data warehouses

  • Programming proficiency in Python, Pyspark, Scala or Java.

  • Experience operating in a cloud-native environment (e.g. Fabric, AWS, GCP, or Azure).

  • Excellent stakeholder management and communication skills.

  • A strategic mindset, with a practical approach to delivery and prioritisation.

  • Proven success with modern data infrastructure: distributed systems, batch and streaming pipelines.

  • Experience building, defining, and owning data models, data lakes, and data warehouses.

  • Exposure to data science concepts and techniques is highly desirable.

  • Strong problem-solving skills and attention to detail.

  • Experience with MS Fabric would be beneficial but it's not essential.

    This is a hybrid role based in Central / West London with the flexibility to work from home 2 or 3 days per week. Our client can offer an excellent career development opportunity whilst working in a modern and vibrant environment. Salary will be dependent on experience and likely to be in the region of £85,000 - £95,000 + an attractive bonus scheme.

    For further information, please send your CV to Wayne Young at Young's Employment Services Ltd. YES are operating as both a recruitment Agency and Recruitment Business

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.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.