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- Tasks: Design and optimise data pipelines, lead projects, and collaborate with clients to solve data challenges.
- Company: Join a fast-growing data consultancy transforming data-driven decision-making for leading organisations.
- Benefits: Enjoy hybrid working, competitive salary, shares, and the chance to innovate in analytics.
- Why this job: Be at the forefront of data engineering, making a real impact while mentoring others.
- Qualifications: Strong Python and SQL skills, experience with Airflow, and a collaborative mindset are essential.
- Other info: Opportunity to work with cutting-edge tools like AWS, Azure, and GCP.
The predicted salary is between 51600 - 61600 £ 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.
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 give you insights into the company culture and expectations.
✨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-driven solutions, 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 analytics and data engineering aligns with their needs, and provide examples of how you've solved data challenges in previous roles.
Showcase Technical Skills: When detailing your experience, focus on your hands-on work with Python libraries like Pandas and Numpy, as well as your knowledge of orchestration tools like Airflow. Be specific about the impact of your contributions in past projects.
Prepare for Interviews: If you get an interview, be ready to discuss technical concepts clearly and concisely. Prepare to explain how you've collaborated with clients and led technical discussions, as this role requires strong communication skills with 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 prowess and problem-solving abilities.
✨Understand the Business Context
Since this 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 industry to tailor your responses during the interview.
✨Demonstrate Leadership and Collaboration
Highlight any experience you have in leading projects or mentoring junior team members. Discuss how you’ve collaborated with cross-functional teams to deliver successful outcomes, as this aligns with the role's requirements.
✨Prepare for Technical Conversations
Expect to engage in discussions that require you to explain complex technical concepts to non-technical stakeholders. Practice simplifying your explanations and using relatable analogies to ensure clarity and understanding.
