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- 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 and making a real impact.
- Qualifications: Experience in ETL/ELT pipelines, Python, SQL, and data modelling is essential.
- Other info: This role offers hybrid/remote work options and a chance to work with senior stakeholders.
The predicted salary is between 36000 - 60000 £ per year.
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 will 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.
We are 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.
Contact Detail:
Mccabe & Barton Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Data Engineer (City of London)
✨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 with these tools can set you apart during discussions.
✨Tip Number 2
Prepare to discuss your experience with ETL/ELT pipelines and data modelling. Be ready to share examples of how you've designed and implemented data solutions that have driven business value, as this will demonstrate your practical knowledge.
✨Tip Number 3
Highlight any previous work in data governance or democratized data environments. Being able to articulate your understanding of data ownership, quality, and security standards will resonate well with the hiring team.
✨Tip Number 4
Showcase your collaboration and communication skills by preparing to discuss how you've worked with senior stakeholders in the past. Providing examples of how you've guided business users in exploring data independently can illustrate your ability to empower others.
We think you need these skills to ace Analytics Data Engineer (City of London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the job description. Emphasise your technical expertise in ETL/ELT pipelines, Python, SQL, and Power BI, as these are crucial for the role.
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, demonstrating your problem-solving mindset.
Showcase Your Technical Skills: In your application, include examples of your work with data modelling and any experience you have in democratized data environments. This will help illustrate your capability to drive governance and enable self-service analytics.
Highlight Collaboration Experience: Since the role requires effective communication with senior stakeholders, provide examples of how you've collaborated with teams in the past. This could include training business users or presenting data solutions to non-technical audiences.
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 organisation and understand their data transformation goals. Be ready to explain how your skills can contribute to their objectives, particularly in creating self-service analytics capabilities.
✨Demonstrate Collaboration Abilities
Highlight your experience working with senior stakeholders and cross-functional teams. Prepare to discuss how you’ve effectively communicated complex data solutions and empowered business users in previous roles.
✨Prepare for Problem-Solving Scenarios
Expect to face analytical challenges during the interview. Practice articulating your thought process when tackling complex data issues, showcasing your problem-solving mindset and ability to deliver high-performance code.
