At a Glance
- Tasks: Transform raw data into actionable insights and support self-service analytics.
- Company: Join Babble, a leader in cloud-based technology solutions.
- Benefits: Enjoy competitive pay, 12 paid holidays, and parental leave.
- Other info: Dynamic work environment with opportunities for growth and collaboration.
- Why this job: Be part of an innovative team making a real impact in customer experience.
- Qualifications: Experience in analytics engineering, strong SQL skills, and familiarity with Snowflake.
The predicted salary is between 60750 - 74250 £ per year.
Babble is a Customer Experience-led growth business. As the ultimate Trusted Advisor for cloud-based technologies, we specialize in empowering businesses with the best solutions from the world's leading providers. We are specialists in:
- Microsoft Modern Work
- Cybersecurity
- Mobile Solutions
- Communications & Connectivity
With thousands of customers, we're just scratching the surface of our potential. Many of our clients currently benefit from one or two of our technologies, presenting an incredible opportunity to introduce them to our full suite of market-leading solutions. Join us on this exciting journey and let's achieve greatness together!
Are you passionate about telecoms? Do you thrive in a fast-paced, innovative environment? At Babble, we’re on a mission to be our customer’s most trusted advisor for cloud-based technologies. We’re looking for passionate and talented individuals to join our dynamic team.
What will you be doing?
We're looking for an Analytics Engineer to turn raw data into trusted, business-ready datasets that power self-service reporting, analytics, and decision-making. Working between Data Engineering, Data Product Owner, and the business, you'll design and maintain analytical data models within our Snowflake data Lakehouse, curate governed datasets and support the ongoing maintenance and evolution of the enterprise semantic layer to ensure it remains accurate, scalable, and easy to use. You'll work closely with Data Product Owner, Business Analysts, and Business Stakeholders to translate agreed business definitions into trusted analytical assets that power OMNI, our enterprise analytics platform.
Key Responsibilities:
- Design, build, and maintain dimensional and analytical data models within Snowflake.
- Develop reusable, business-ready datasets using modern ELT practices.
- Create and maintain fact and dimension models that support OMNI and wider reporting requirements.
- Optimise analytical models for performance, scalability, and usability.
- Support the delivery of trusted data products across key business domains.
- Implement data quality checks and validation controls to ensure analytical datasets remain accurate, trusted, and fit for purpose.
Semantic Layer, Context Layer & Metrics:
- Maintain and enhance the context layer and enterprise semantic layer that support OMNI and wider analytical reporting.
- Implement and maintain approved KPI, metric, and hierarchy definitions.
- Ensure metrics are consistently applied across OMNI and any other relevant reporting solutions.
- Support the performance, governance, documentation, and ongoing maintenance of context layer and semantic layer components.
- Contribute to semantic modelling standards and best practices.
- Assess and manage the impact of business or source system changes on analytical models and reporting assets.
Analytics Enablement:
- Deliver curated datasets that simplify reporting and analysis across OMNI.
- Enable self-service analytics through well-designed and governed data structures.
- Support BI Developers, Analysts, and business users with trusted data products.
- Improve consistency, adoption, and usability of OMNI as the organisation's strategic analytics platform.
Stakeholder Collaboration:
- Partner with Business Analyst, Data Product Owner, and Business Stakeholders to understand analytical requirements and opportunities.
- Work with Data Product Owner to implement agreed business definitions within the semantic layer.
- Provide guidance on data modelling, analytics engineering, and self-service analytics best practices.
- Support business adoption of OMNI through trusted and consistent analytical datasets.
Qualifications:
- Experience as an Analytics Engineer, BI Developer, Data Analyst, or similar role.
- Advanced SQL skills.
- Strong understanding of dimensional modelling and modern data warehousing principles.
- Experience working with Snowflake or similar cloud-based analytical platforms.
- Experience working with a modern cloud-based data visualisation tool such as Omni, Tableau, Sigma, or similar.
- Experience designing and maintaining semantic models and business-facing analytical datasets.
- Knowledge of ELT and modern analytics engineering practices.
- Strong stakeholder engagement and communication skills.
- Ability to translate business requirements into scalable analytical solutions.
Additional Information:
- Public Holidays: 12 paid bank holidays
- Parental Leave: Statutory maternity and paternity leave
Analytics Engineer employer: Babble
Babble is an exceptional employer, offering a dynamic work environment in High Wycombe that fosters innovation and collaboration. With a strong focus on employee growth, we provide comprehensive benefits including a generous pension scheme, health insurance options, and a hybrid working policy that promotes work-life balance. Join us to be part of a forward-thinking team dedicated to delivering top-notch cloud-based solutions while enjoying opportunities for professional development and annual celebrations.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Babble!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Analytics Engineer at Babble.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Babble.
✨Apply Directly through Our Website
When you find a suitable opening like Analytics Engineer at Babble, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Analytics Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Babble, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Babble. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Babble
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Babble!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
