At a Glance
- Tasks: Extract and transform data to create reliable datasets for metrics dashboards.
- Company: Join Add Agility, a consultancy solving real-world problems for clients.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Hands-on role with excellent career development in a dynamic environment.
- Why this job: Make an impact by turning complex data into actionable insights for tech teams.
- Qualifications: Experience with Azure DevOps and data engineering skills required.
The predicted salary is between 45000 - 55000 £ per year.
Commitment: 5 days per week
Location: Hybrid – 2 days per week in London, 3 days remote
Requirements: Have the right to work in the UK.
The core challenge is to extract data from Azure DevOps, transform it in Databricks and create reliable, reusable datasets that can power a comprehensive metrics dashboard across a large number of technology teams. You will be working for Add Agility, a consultancy that solves problems for clients and working with one or more of our clients based in central London. This is primarily a Databricks and data engineering role. The wider environment also includes Palantir Foundry data visualisation, but deep Palantir expertise is not required. Other members of the team can support with Palantir-specific elements.
What You'll Be Doing:
- Extracting data from Azure DevOps
- Working with Azure DevOps APIs and/or Analytics data
- Building robust ingestion pipelines into Databricks
- Understanding and transforming Azure DevOps work-item and historical data
- Creating clean, reusable datasets for reporting and analytics
- Building calculations for engineering and delivery metrics
- Creating reliable data models and tables that can be consumed by the dashboard layer
- Improving data quality, validation and monitoring
- Working alongside product, engineering and client stakeholders to turn metric definitions into working data products
- Creating components and approaches that can be reused across future implementations
This is a hands-on engineering role. We need someone who can understand the underlying Azure DevOps data, build the pipelines and transformations, and turn it into a robust data layer from which meaningful engineering metrics can be calculated.
The initial engagement runs five days per week until Christmas 2026, with two days per week working with the team in London and three days remote.
Finance Data Analyst in London employer: Y-Axis
At Rolls-Royce, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration within our multi-disciplinary teams in Bristol. Our commitment to employee growth is evident through comprehensive training programs and opportunities to lead design activities, ensuring that every team member can thrive while contributing to groundbreaking gas turbine technologies. With generous benefits such as annual leave, group income protection, and a focus on inclusivity, we create an environment where individuals are valued and empowered to make a meaningful impact.
StudySmarter Expert Advice🤫
We think this is how you could land Finance Data Analyst in London
✨Get Involved in Data Science Meetups
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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 Y-Axis.
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We think you need these skills to ace Finance Data Analyst in London
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 Y-Axis, 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 Y-Axis. 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 Y-Axis
✨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 Y-Axis!
✨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.
