At a Glance
- Tasks: Design and build scalable data pipelines using Azure Data Factory and Snowflake.
- Company: Join a forward-thinking company focused on innovative data solutions.
- Benefits: Competitive daily rate, hybrid work model, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on continuous improvement and agile practices.
- Why this job: Make an impact by developing data products that drive business decisions.
- Qualifications: Experience with Azure Data Factory, SQL, and cloud data platforms required.
The predicted salary is between 63000 - 77000 £ per year.
We are looking for a skilled Data Engineer with hands-on experience in designing, building, and supporting scalable data pipelines and data products across modern cloud data platforms. The ideal candidate should have strong experience with Azure Data Factory, Snowflake, and DataOps, along with a good understanding of Data Product concepts, data integration, orchestration, and engineering best practices. Power BI experience is desirable and would be considered a good-to-have skill.
Key Responsibilities
- Design, build, and maintain scalable data pipelines using Azure Data Factory.
- Develop robust ETL/ELT processes to ingest, transform, and publish data across enterprise platforms.
- Work with structured and semi-structured data, applying appropriate data modelling, validation, and quality checks.
- Write and optimise SQL for data transformation, reconciliation, and performance tuning.
Cloud Data Platform & DataOps
- Develop and support data solutions on Snowflake as a core cloud data platform.
- Use DataOps.live practices and tooling to support version-controlled, automated, and repeatable data deployments.
- Collaborate with engineering and platform teams to implement CI/CD, environment management, and release controls for data assets.
- Support monitoring, troubleshooting, and continuous improvement of data pipelines and platform processes.
Data Product Development
- Contribute to the design and delivery of reusable Data Products aligned to business and analytical needs.
- Apply data product principles such as ownership, discoverability, quality, reusability, and clear documentation.
- Work with business stakeholders, analysts, and technical teams to understand data requirements and translate them into reliable data solutions.
- Ensure data outputs are trusted, governed, and suitable for downstream reporting, analytics, and operational use cases.
Good-to-Have: Reporting & Analytics
- Familiarity with Power BI reporting, semantic models, datasets, and dashboard development.
- Ability to support reporting teams by providing well-structured, performance-optimised data models.
- Understanding of business KPIs and how data engineering outputs support analytics and decision-making.
Preferred Skills & Experience
- Hands-on experience with Azure Data Factory, including pipeline orchestration, triggers, linked services, datasets, and monitoring.
- Strong SQL skills and experience working with cloud data platforms such as Snowflake.
- Experience with DataOps.live or similar DataOps/DevOps tooling for automated deployment and environment management.
- Understanding of Data Product concepts, metadata, governance, and documentation practices.
- Good-to-have experience in Power BI for reporting, dashboards, and data visualisation.
- Knowledge of Python or another scripting language would be advantageous.
Key Competencies
- Strong analytical and problem-solving skills.
- Ability to build reliable, scalable, and maintainable data solutions.
- Good communication skills with the ability to work across business, data, and engineering teams.
- Attention to detail, especially around data quality, reconciliation, and documentation.
- Ability to work in an agile delivery environment and manage priorities effectively.
Sr Data Engineer employer: Experis
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StudySmarter Expert Advice🤫
We think this is how you could land Sr Data Engineer
✨Tap into Online Data Science Communities
Join online communities focused on data science like Kaggle, LinkedIn groups, or Reddit threads. These are goldmines for temporary gigs, as you can network with professionals and potentially hear about opportunities at companies like Experis before they're even advertised!
✨Show Off Your Skills With Projects
Got some cool data science projects? Showcase them on platforms like GitHub or create a personal portfolio website. This visibility is crucial for landing temporary roles—let recruiters see your actual skills in action, which can set you apart from the crowd.
✨Check Out Specialist Job Boards
For temp roles, hit up job boards dedicated to tech and data science, like Stack Overflow Jobs or DataJobs. These platforms often feature openings that you won’t find on general job sites, including contracts with companies like Experis.
✨Leverage University Resources
If you're currently at uni or recently graduated, tap into your school's career services. They often have connections with companies looking for temporary data science interns or contract workers, and they might even host job fairs with employers like Experis.
We think you need these skills to ace Sr Data Engineer
Some tips for your application 🫡
Highlight Your Data Projects:When applying for a temporary data science role at Experis, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.
Emphasise Your Analytical Skills:In your CV and cover letter, focus on the specific analytical skills that are key to data science. Mention any experience with statistical tools, programming languages like Python or R, and data visualisation software. Don't forget to include any certifications that may bolster your expertise!
Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to Experis, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.
Craft a Unique Data-Driven Cover Letter:Instead of the usual generic cover letter, spice it up with some data! Maybe you’ve improved a process by 20% in a past role or cleaned a dataset with over a million entries. Use these stats to your advantage to grab Experis’s attention and show the tangible impact of your work.
How to prepare for a job interview at Experis
✨Showcase Your Analytical Skills
For a data science gig, it's crucial to demonstrate your analytical abilities. Be ready to discuss previous projects and the methodologies you used. Think about how you can quantify your impact—did your analysis improve efficiency or save costs? These are the stories that will stick with interviewers at Experis.
✨Brush Up on Technical Skills
You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.
✨Highlight Your Adaptability
Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at Experis.
✨Prepare a Portfolio of Your Work
Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at Experis.
