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- Tasks: Design and optimise data pipelines using Python, SQL, and Airflow.
- Company: Join a fast-growing data consultancy transforming data-driven decision-making.
- Benefits: Enjoy hybrid working, competitive salary, and share options.
- Why this job: Lead innovative projects and solve real-world business challenges in a collaborative environment.
- Qualifications: Experience with Python, SQL, Airflow, and a background in analytics or data engineering required.
- Other info: Opportunity to mentor juniors and shape best practices in analytics engineering.
The predicted salary is between 48000 - 56000 £ per year.
We’re hiring on behalf of a fast-growing data consultancy that’s transforming how leading organisations make high-impact, data-driven decisions. This is a standout opportunity for an ambitious Data Analytics Engineer to take a leading role in shaping data systems whilst solving real-world business challenges.
This isn’t just another engineering position. You’ll be sitting at the crossroads of data engineering, advanced analytics, and technical strategy. Whether it’s building robust data pipelines, architecting scalable workflows, or working hand-in-hand with clients to translate data into actionable insights, the work is cutting-edge.
What You’ll Be Doing:
- Designing, building, and optimising scalable data pipelines using Python, SQL, and Airflow
- Leading the technical delivery of projects across diverse industry verticals
- Collaborating directly with clients to understand their data challenges and craft tailored solutions
- Providing mentorship and technical guidance to junior team members
- Shaping best practices and driving innovation in how the team approaches analytics engineering
What skills you need:
- Strong hands-on experience with Python (including libraries like Pandas/Numpy)
- Deep working knowledge of SQL for complex data querying and transformation
- Proven experience working with orchestration tools such as Airflow
- A background in analytics or data engineering, ideally in client-facing or cross-functional teams
- Ability to lead technical conversations and articulate solutions to non-technical stakeholders
- A collaborative mindset and genuine curiosity around solving data challenges
Tech Stack Snapshot:
- Python, SQL, Airflow
- AWS, Azure, & GCP depending on the project
- Bonus: Experience with dbt, Snowflake, or Looker would be amazing, as this business uses these tools.
If you’re ready to step into a role where you can lead, innovate, and make a tangible difference - this is it. Click "APPLY" to be considered.
Data Analytics Engineer employer: Revybe IT Recruitment Ltd
Contact Detail:
Revybe IT Recruitment Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analytics Engineer
✨Tip Number 1
Familiarise yourself with the specific tools mentioned in the job description, especially Python, SQL, and Airflow. Consider working on personal projects or contributing to open-source projects that utilise these technologies to showcase your hands-on experience.
✨Tip Number 2
Network with professionals in the data analytics field, particularly those who work in consultancy roles. Attend industry meetups or webinars to connect with potential colleagues and learn more about the challenges they face, which can help you tailor your approach during interviews.
✨Tip Number 3
Prepare to discuss real-world examples of how you've solved data challenges in previous roles. Be ready to articulate your thought process and the impact of your solutions, as this will demonstrate your ability to lead technical conversations effectively.
✨Tip Number 4
Showcase your collaborative mindset by highlighting experiences where you've worked cross-functionally or mentored others. This is crucial for a role that involves client interaction and teamwork, so be sure to emphasise these skills during discussions.
We think you need these skills to ace Data Analytics Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, SQL, and Airflow. Include specific projects where you've built data pipelines or worked on analytics engineering to demonstrate your relevant skills.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention how your background aligns with their needs, particularly your experience in client-facing roles and your ability to lead technical discussions.
Showcase Relevant Projects: If you have any personal or professional projects that involved data engineering or analytics, summarise them in your application. Highlight the tools you used, such as Python libraries or orchestration tools like Airflow.
Prepare for Technical Questions: Anticipate technical questions related to your experience with data pipelines and analytics. Be ready to discuss your problem-solving approach and how you've collaborated with clients to address their data challenges.
How to prepare for a job interview at Revybe IT Recruitment Ltd
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with Python, SQL, and Airflow. Bring examples of projects where you've built data pipelines or optimised workflows, as this will demonstrate your technical prowess and relevance to the role.
✨Understand the Business Context
Research the company and its clients to understand their data challenges. Be ready to articulate how your skills can help solve these issues, showing that you can translate technical solutions into business value.
✨Prepare for Technical Conversations
Expect to lead discussions on technical topics during the interview. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with non-technical stakeholders.
✨Demonstrate a Collaborative Mindset
Highlight your experience working in cross-functional teams and mentoring junior members. Share examples of how you've collaborated with others to drive innovation and best practices in analytics engineering.
