At a Glance
- Tasks: Transform raw data into trusted datasets for impactful reporting and analytics.
- Company: Growing financial services organisation in Manchester with a focus on data innovation.
- Benefits: Competitive salary, hybrid work, annual bonus, private healthcare, and 25 days leave.
- Other info: Genuine opportunities for career progression and ongoing professional development.
- Why this job: Join a collaborative team and tackle complex data challenges with modern technologies.
- Qualifications: Experience in analytics engineering, strong SQL skills, and knowledge of cloud data platforms.
The predicted salary is between 60000 - 80000 £ per year.
Location: Manchester (Hybrid, 2-3 days per week in the office)
Salary: £60,000 - £80,000 DOE
Industry: Financial Services
The Opportunity
I'm currently working with a growing financial services organisation based in Manchester that is looking to hire an Analytics Engineer to strengthen its data and analytics function. This is an exciting opportunity to join a business investing heavily in its data platform, where you'll play a key role in transforming raw data into trusted, business-ready datasets that support reporting, analytics and strategic decision-making across the organisation. Working closely with Data Engineers, BI Analysts and key business stakeholders, you'll be responsible for developing scalable data models, improving data quality and enabling self-service analytics across multiple business functions. This role would suit someone who enjoys solving complex data challenges, working with modern data technologies and collaborating with both technical and non-technical teams to deliver meaningful business outcomes.
Key Responsibilities
- Develop and maintain scalable data models to support business intelligence and analytics.
- Build and optimise ELT pipelines that transform data into clean, reliable datasets.
- Work with stakeholders across the business to gather reporting and analytics requirements.
- Ensure data quality, consistency and governance across multiple data sources.
- Collaborate with Data Engineers to improve and optimise the organisation's modern data platform.
- Support the development of dashboards and reporting solutions used by senior leadership.
- Improve the accessibility and usability of data for business users through well-structured data models.
- Document data models, transformation logic and analytics processes.
- Identify opportunities to improve existing reporting, automation and data workflows.
- Promote best practices around analytics engineering, testing, documentation and data quality.
Essential Skills and Experience
- Previous experience as an Analytics Engineer, Analytics Developer, Data Engineer or similar role.
- Strong SQL skills with experience writing complex and optimised queries.
- Experience building and maintaining data models using dbt or similar transformation tools.
- Experience working with modern cloud data platforms such as Snowflake, BigQuery, Azure Synapse or Databricks.
- Strong understanding of ELT processes and data modelling principles.
- Experience working with version control tools such as Git.
- Ability to translate business requirements into scalable technical solutions.
- Strong communication and stakeholder management skills.
- Excellent analytical and problem-solving abilities.
Desirable Skills
- Experience within financial services, banking, fintech or another regulated industry.
- Knowledge of Python for data transformation or automation.
- Experience with orchestration tools such as Airflow.
- Exposure to BI platforms including Power BI, Tableau or Looker.
- Understanding of data governance, data quality and regulatory reporting requirements.
- Experience working within Agile delivery teams.
- Familiarity with CI/CD and modern software engineering practices.
Package and Benefits
- £60,000 - £80,000 salary (depending on experience).
- Hybrid working (2-3 days per week in the Manchester office).
- Annual bonus.
- Competitive pension contribution.
- Private healthcare.
- 25 days annual leave plus bank holidays.
- Ongoing learning and professional development opportunities.
- Opportunity to work with a modern cloud data stack.
- Join a collaborative team with genuine opportunities for progression and career development.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer in Manchester
✨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 Oscar!
✨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 Oscar.
✨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 Oscar.
✨Apply Directly through Our Website
When you find a suitable opening like Analytics Engineer at Oscar, 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 in Manchester
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 Oscar, 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 Oscar. 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 Oscar
✨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 Oscar!
✨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.
