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- Tasks: Design and optimise data pipelines, lead projects, and mentor junior team members.
- Company: Join a fast-growing data consultancy transforming data-driven decision-making for top organisations.
- Benefits: Enjoy hybrid working, competitive salary, shares, and a collaborative work environment.
- Why this job: Be at the forefront of data innovation, solving real-world challenges with cutting-edge technology.
- Qualifications: Strong Python and SQL skills, experience with Airflow, and a background in analytics or data engineering.
- Other info: Opportunity to work with diverse industries and shape best practices in analytics engineering.
The predicted salary is between 48000 - 84000 £ per year.
Central London - 2 days a week in the office - hybrid working. £60,000 - £70,000 depending on experience + shares. 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 Analyst 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 Analyst (Analytics) 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 Analyst (Analytics)
✨Tip Number 1
Familiarise yourself with the specific tools mentioned in the job description, such as 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, especially those who work in consultancy roles. Attend industry meetups or webinars to connect with potential colleagues and learn about their experiences, which can provide valuable insights for your application.
✨Tip Number 3
Prepare to discuss real-world business challenges you've faced in previous roles or projects. Be ready to articulate how you approached these challenges using data analytics, as this will demonstrate your problem-solving skills and ability to work with clients.
✨Tip Number 4
Showcase your collaborative mindset by highlighting any experience you have working in cross-functional teams. Be prepared to discuss how you’ve effectively communicated technical concepts to non-technical stakeholders, as this is crucial for the role.
We think you need these skills to ace Data Analyst (Analytics)
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 designed 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 in data analytics aligns with their mission of transforming data-driven decision-making for clients.
Showcase Relevant Projects: If you have experience with tools like dbt, Snowflake, or Looker, be sure to mention these in your application. Provide examples of how you've used these technologies to solve real-world business challenges.
Prepare for Technical Questions: Anticipate technical questions related to data engineering and analytics during the interview process. Be ready to discuss your approach to building scalable data pipelines and how you communicate complex solutions to non-technical stakeholders.
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 designed and optimised data pipelines, as this will demonstrate your technical capabilities and problem-solving skills.
✨Understand the Business Context
Since the role involves working closely with clients, it's crucial to articulate how your technical solutions can address their specific data challenges. Research the company and its clients to tailor your responses during the interview.
✨Demonstrate Leadership and Mentorship
Highlight any experience you have in leading projects or mentoring junior team members. This role requires collaboration and guidance, so showcasing your ability to lead technical conversations will set you apart.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about past experiences where you've faced data challenges and be ready to explain your thought process and the outcomes.
