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For Companies At a Glance
- Tasks: Design and deliver data products for self-service analytics using cutting-edge technologies.
- Company: Join a leading Financial Services organisation driving a major Data Transformation initiative.
- Benefits: Enjoy competitive salary, flexible working arrangements, and opportunities for professional growth.
- Why this job: Be at the forefront of innovation, empowering teams with data-driven insights and solutions.
- Qualifications: Experience in ETL/ELT pipelines, Python, SQL, and data modelling is essential.
- Other info: This is a permanent role with a focus on collaboration and continuous improvement.
The predicted salary is between 36000 - 60000 £ per year.
Location: London (Hybrid/Remote available)
Salary: £45,000 - £70,000 based on experience
The Opportunity
A leading Financial Services organisation is seeking exceptional Analytics Data Engineers to join their ambitious Data Transformation initiative. This is a permanent role offering competitive compensation and flexible working arrangements. As an Analytics Data Engineer, you will be at the forefront of their data transformation, designing and delivering data products that empower business teams with self-service analytics capabilities. You'll leverage cutting-edge technologies, including Snowflake, Power BI, Python, and SQL to create scalable, intuitive data solutions that drive business value.
Key Responsibilities
- Build Data Products: Collaborate with business domains to design and develop ETL/ELT pipelines and dimensional models optimised for Power BI
- Drive Governance: Define and enforce data ownership, quality, and security standards within the Data Mesh architecture
- Enable Self-Service: Create intuitive data models and provide training to empower business users to explore data independently
- Own the Data Lifecycle: Take end-to-end responsibility for data products, from conception to deployment and continuous improvement
- Champion Innovation: Stay current with the latest trends and advocate for best practices across the organisation
The Ideal Candidate
We're looking for a curious, organised, and outcome-driven professional with a passion for data and collaboration. You should bring:
- Technical Expertise: Proven experience coding ETL/ELT pipelines with Python, SQL, or ETL tools, and proficiency in Power BI, Tableau, or Qlik
- Data Modelling Skills: Strong knowledge of dimensional modelling and database principles
- Governance Experience: Track record of working in democratized data environments, establishing controls and guardrails
- Collaboration & Communication: Ability to work effectively with senior stakeholders, present data solutions, and guide business users
- Problem-Solving Mindset: Exceptional analytical skills to tackle complex data challenges and deliver reliable, high-performance code
If you are open to exploring this role further, please respond to this advert with your latest CV for review.
Analytics Data Engineer employer: McCabe & Barton
Contact Detail:
McCabe & Barton Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Data Engineer
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Snowflake, Power BI, Python, and SQL. Having hands-on experience or projects showcasing your skills in these areas can significantly boost your chances of standing out.
✨Tip Number 2
Network with professionals in the financial services sector, especially those working in data roles. Engaging with them on platforms like LinkedIn can provide insights into the company culture and expectations, which can be invaluable during interviews.
✨Tip Number 3
Prepare to discuss your experience with data governance and self-service analytics. Be ready to share examples of how you've implemented data quality standards or empowered users to explore data independently, as these are key responsibilities for the role.
✨Tip Number 4
Stay updated on the latest trends in data engineering and analytics. Being able to discuss recent innovations or best practices during your interview will demonstrate your passion for the field and your commitment to continuous learning.
We think you need these skills to ace Analytics Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the role of an Analytics Data Engineer. Emphasise your expertise in ETL/ELT pipelines, Python, SQL, and any experience with Power BI or similar tools.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data and collaboration. Mention specific projects where you've successfully built data products or improved data governance, and explain how these experiences make you a great fit for the role.
Showcase Your Technical Skills: In your application, include examples of your technical expertise. Discuss any relevant projects where you've used Snowflake, Power BI, or other technologies mentioned in the job description to create scalable data solutions.
Highlight Problem-Solving Abilities: Demonstrate your analytical skills by providing examples of complex data challenges you've tackled in the past. Explain your approach to problem-solving and how it led to successful outcomes in previous roles.
How to prepare for a job interview at McCabe & Barton
✨Showcase Your Technical Skills
Be prepared to discuss your experience with ETL/ELT pipelines, Python, SQL, and Power BI. Bring examples of past projects where you successfully implemented these technologies, as this will demonstrate your technical expertise.
✨Understand the Business Context
Research the financial services industry and understand how data analytics drives business decisions. This knowledge will help you articulate how your role as an Analytics Data Engineer can add value to the organisation.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills and ability to handle complex data challenges. Think of specific scenarios from your past experiences where you overcame obstacles and delivered results.
✨Emphasise Collaboration and Communication
Highlight your ability to work with senior stakeholders and guide business users. Prepare examples of how you've effectively communicated technical concepts to non-technical audiences, as this is crucial for enabling self-service analytics.
