At a Glance
- Tasks: Own and evolve data pipelines, ensuring reliable and scalable analytics.
- Company: Join a forward-thinking company shaping the future of data engineering.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with opportunities to influence and innovate.
- Why this job: Make a real impact on enterprise analytics and shape data engineering practices.
- Qualifications: Strong experience in data engineering, SQL, and cloud platforms required.
The predicted salary is between 60000 - 80000 £ per year.
We’re looking for a Senior Data Engineer to take ownership of the data engineering layer that underpins enterprise reporting and analytics. This is a hands‑on role focused on building and running the pipelines, transformations, curated datasets, and quality controls that turn operational data into trusted, usable assets for decision‑making. You’ll play a key role in shaping how data engineering is done within a growing analytics function. In the near term, you’ll strengthen and evolve the platform foundations behind critical analytics use cases. Over time, you’ll help build more reusable, scalable data services that can support a broader range of analytics and digital needs.
Key Responsibilities
- Own and evolve core data pipelines, transformation logic, and curated datasets that support enterprise reporting and analytics.
- Design, build, and maintain scalable data models across warehouse / lakehouse environments, with a focus on reliability, clarity, and reuse.
- Implement strong data quality, validation, monitoring, and operational controls so critical data assets remain trusted and resilient.
- Integrate data from multiple source systems into well‑structured datasets for analytics and reporting use cases.
- Work closely with analytics, BI, platform, and architecture colleagues to ensure downstream reporting and analytics sit on stable engineering foundations.
- Apply strong engineering discipline through CI/CD, version control, documentation, and repeatable delivery patterns.
- Improve performance, maintainability, and scalability of data pipelines and models as the platform grows.
- Help establish reusable patterns and standards for data engineering across the analytics function.
- Support the evolution of the analytics platform so it can serve not only reporting needs today, but broader analytics and digital use cases over time.
Essential Experience / Skills
- Strong senior‑level data engineering experience building and maintaining scalable data platforms and pipelines.
- Strong SQL plus Python / PySpark or equivalent experience for ingestion, transformation, and validation work.
- Experience with cloud data platforms, orchestration tooling, and modern warehouse / lakehouse patterns.
- Experience designing and maintaining curated datasets and data models for analytics use cases.
- Experience implementing data quality, monitoring, validation, and secure / governed data handling.
- Good engineering discipline, including CI/CD, version control, documentation, and repeatable delivery practices.
- Ability to work with technical and non-technical stakeholders to translate business needs into robust technical solutions.
Desirable Experience / Skills
- Experience with Microsoft Fabric, Azure-based data engineering, or similar modern cloud data environments.
- Experience supporting analytics or enterprise reporting environments with high expectations around trust, governance, and continuity.
- Familiarity with business‑critical data domains such as finance, operations, or people data.
- Experience helping teams raise engineering maturity through shared standards, patterns, and service ownership.
Why join / opportunity
- Take real ownership of important data assets at the heart of enterprise analytics.
- Help shape how data engineering is done within a growing analytics capability.
- Work on meaningful platform foundations that support trusted reporting today and more reusable analytics services over time.
- Influence the move from fragmented data workflows toward more robust, scalable, and well-governed engineering patterns.
- Partner with a broad set of stakeholders across analytics, BI, platform, and digital teams in a role with clear impact and room to grow.
- Build something lasting: not just pipelines, but a stronger engineering foundation for future analytics delivery.
Senior Data Engineer employer: SLR Consulting
Join a forward-thinking company that values innovation and collaboration, where as a Senior Data Engineer, you will have the opportunity to take ownership of critical data assets and shape the future of our analytics capabilities. Our supportive work culture fosters continuous learning and growth, providing you with the tools and resources to enhance your skills while working on meaningful projects that have a direct impact on decision-making. Located in a vibrant area, we offer a dynamic environment that encourages creativity and teamwork, making it an excellent place for professionals looking to make a difference.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field and let them know you're on the lookout for opportunities. You never know who might have the inside scoop on a role that’s perfect for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving SQL, Python, or cloud platforms. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering scenarios. Be ready to discuss how you've built and maintained scalable data pipelines, and don’t forget to highlight your experience with data quality and governance.
✨Tip Number 4
Apply through our website! We’re always on the lookout for talented individuals like you. Plus, applying directly gives you a better chance to stand out and show us why you’re the right fit for the team.
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the Senior Data Engineer role. Highlight your experience with data pipelines, SQL, and any cloud platforms you've worked with. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to tell us why you're passionate about data engineering and how you can contribute to our analytics function. Be sure to mention specific projects or experiences that relate to the job description.
Showcase Your Technical Skills:Don’t hold back on showcasing your technical prowess! Include examples of your work with Python, PySpark, or any orchestration tools. We love seeing how you've implemented data quality and governance in past roles.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at SLR Consulting
✨Know Your Data Engineering Fundamentals
Brush up on your core data engineering concepts, especially around building and maintaining scalable data platforms. Be ready to discuss your experience with SQL, Python, and cloud data environments, as these will likely come up in the interview.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled challenges in data quality, monitoring, and validation. Highlight your ability to translate complex technical issues into solutions that meet business needs, as this is crucial for the role.
✨Familiarise Yourself with Their Tech Stack
If the company uses specific tools like Microsoft Fabric or Azure, make sure you understand their functionalities. Being able to speak knowledgeably about these technologies will demonstrate your readiness to hit the ground running.
✨Emphasise Collaboration and Communication
Since the role involves working closely with various stakeholders, be prepared to discuss how you've successfully collaborated with both technical and non-technical teams in the past. This will show that you can bridge the gap between engineering and business needs.
