At a Glance
- Tasks: Lead data initiatives and coordinate teams to deliver secure data solutions.
- Company: Join M&G Prudential, a leader in the financial services industry.
- Benefits: Enjoy competitive pay, flexible working, and opportunities for professional growth.
- Other info: Dynamic role with excellent career advancement potential.
- Why this job: Make a real impact by shaping data delivery across innovative cloud platforms.
- Qualifications: Experience in technical delivery and strong collaboration skills required.
The predicted salary is between 63000 - 77000 Β£ per year.
M&G Prudential is seeking a Technical Delivery Manager (Data) to lead end-to-end delivery of data initiatives across our Enterprise Data function.
You will coordinate business, technology and delivery teams to shape outcomes, plan work, manage dependencies and deliver secure, well-governed data solutions.
The role supports cloud data platforms and requires close collaboration with multiple stakeholder groups.
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Data Delivery Leader β Azure & Databricks Platform employer: M&gprudential
M&GPrudential is an excellent employer, offering a dynamic work environment where you can lead a dedicated team and make a significant impact on financial reporting. With a strong commitment to employee well-being, the company provides generous benefits including a pension scheme, extensive annual leave, and comprehensive health coverage, all within a diverse and inclusive culture that fosters professional growth and development.
StudySmarter Expert Adviceπ€«
We think this is how you could land Data Delivery Leader β Azure & Databricks Platform
β¨Get Involved in Data Science Meetups
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We think you need these skills to ace Data Delivery Leader β Azure & Databricks Platform
Some tips for your application π«‘
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. Donβt 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 M&gprudential, 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 youβre 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 M&gprudential. 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 M&gprudential
β¨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!
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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, itβll really make us stand out!
β¨Get Comfortable with Python and R
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β¨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how weβd 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.