At a Glance
- Tasks: Build and operate Databricks infrastructure for automated ML workflows.
- Company: Join DraftKings, a leader in the tech and gaming industry.
- Benefits: Competitive salary, flexible work options, and growth opportunities.
- Other info: Collaborative environment with a focus on innovation and technology.
- Why this job: Make an impact by scaling cloud systems and delivering ML pipelines.
- Qualifications: Experience with Databricks, AWS, and infrastructure as code tools.
The predicted salary is between 60750 - 74250 £ per year.
Draft Kings seeks a Senior Data Platform Engineer to build and operate the Databricks and cloud infrastructure powering automated ML workflows across the business.
This role blends platform ownership with deep infrastructure engineering, collaborating with Data Science, ML Engineering, and Infra teams.
You will own Databricks infrastructure, scale cloud systems with AWS and Ia C tools like Terraform and Pulumi, and help deliver reproducible ML pipelines at scale.
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Senior Data Platform Engineer — ML Infra & Databricks Lead in London employer: DraftKings
DraftKings is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of Boston. With a strong focus on employee growth, we provide ample opportunities for professional development and career advancement, all while being part of a high-performing team dedicated to driving sustainable revenue growth in the exciting iGaming industry.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Platform Engineer — ML Infra & Databricks Lead in London
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We think you need these skills to ace Senior Data Platform Engineer — ML Infra & Databricks Lead 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 DraftKings, 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 DraftKings. 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 DraftKings
✨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, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨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.
