At a Glance
- Tasks: Design and maintain data infrastructure for machine learning, ensuring reliability and efficiency.
- Company: Epic Games creates award-winning games and innovative engine technology across 25 countries.
- Benefits: Enjoy comprehensive benefits including private medical insurance, dental cover, and mental well-being support.
- Why this job: Join a creative team that values innovation and community impact in the gaming industry.
- Qualifications: BSc/MSc in Computer Science or extensive hands-on experience; strong analytical skills required.
- Other info: Epic Games promotes diversity and inclusivity, welcoming applicants from all backgrounds.
The predicted salary is between 48000 - 72000 £ per year.
At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.
Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.
DATA ENGINEERINGOur mission is to provide a world-class platform that empowers the business to leverage data that will enhance, monitor, and support our products. We are responsible for data ingestion systems, processing pipelines, and various data stores all operating in the cloud. We operate at a petabyte scale, and support near real-time use cases as well as more traditional batch approaches.
What You’ll DoYou will be responsible for designing, building, and maintaining our data infrastructure to ensure the reliability and efficiency of our data and systems used by our Machine Learning team. Your role will include creating and maintaining data pipelines that transform and load data from various products and managing the AWS infrastructure for our machine learning platform. Additionally, you will work with engineers, product managers, and data scientists to design and implement robust and scalable data services that support Epic's mission while ensuring our user’s privacy.
In this role, you will:
- Interact with product teams to understand how our safety systems interact with their data systems.
- Design and implement an automated end-to-end ETL process, including data anonymization, to prepare data for machine learning and ad hoc analysis.
- Manage and scale the tools and technologies we use to label data running on AWS.
- Devise database structure and technology for storing and efficiently accessing large data sets (millions of records) of different types (text, images, videos, etc.).
- Use and implement data extraction APIs.
- Write and invoke custom SQL procedures.
- Support data versioning strategies using automated tools.
- Strong analytical background: BSc or MSc in Computer Science/Software Engineering or related subject - candidates without a degree are welcome as long as they have extensive hands-on experience.
- Experience in ETL technical design, automated data quality testing, QA and documentation, data warehousing, and data modeling.
- Experience with Python for interaction with Web Services (e.g., Rest and Postman).
- Experience with using and developing data APIs.
- Experience using AWS, Snowflake, or other comparable large-scale analytics platforms.
- Experience monitoring and managing databases (we use Elasticsearch / MongoDB / PostgreSQL).
- Experience with SQL.
- Experience with data versioning tools.
- Experience developing and maintaining data infrastructure for ETL pipelines, such as Apache Airflow.
We pay 100% for benefits except for PMI (for dependents). Our current benefits package includes pension, private medical insurance, health care cash plan, dental insurance, disability and life insurance, critical illness, cycle to work scheme, flu shots, health checks, and meals. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents.
ABOUT USEpic Games spans across 25 countries with 46 studios and 4,500+ employees globally. For over 25 years, we’ve been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.
Like what you hear? Come be a part of something Epic!
Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer.
Machine Learning Data Engineer (Basé à London) employer: Golden Bees
Contact Detail:
Golden Bees Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Data Engineer (Basé à London)
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as AWS, Apache Airflow, and SQL. Having hands-on experience or projects that showcase your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Network with current employees or professionals in the data engineering field, especially those who work with machine learning. Engaging in conversations about their experiences at Epic can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Prepare to discuss your previous projects involving ETL processes and data pipelines. Be ready to explain your role, the challenges you faced, and how you overcame them, as this will demonstrate your problem-solving abilities and technical expertise.
✨Tip Number 4
Showcase your passion for data engineering and machine learning by staying updated on industry trends and advancements. Mentioning recent developments or technologies during your interactions can highlight your enthusiasm and commitment to the field.
We think you need these skills to ace Machine Learning Data Engineer (Basé à London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data engineering, ETL processes, and AWS. Use keywords from the job description to demonstrate that you meet the specific requirements of the Machine Learning Data Engineer role.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data engineering and how your skills align with Epic's mission. Mention specific projects or experiences that showcase your ability to design and maintain data infrastructure.
Showcase Technical Skills: Clearly outline your technical skills related to Python, SQL, and AWS in your application. Provide examples of how you've used these technologies in past roles, especially in relation to data pipelines and analytics.
Highlight Collaboration Experience: Since the role involves working with product teams and data scientists, emphasise any previous collaborative projects. Describe how you contributed to team success and how you can bring that collaborative spirit to Epic.
How to prepare for a job interview at Golden Bees
✨Understand the Role
Make sure you have a solid grasp of what a Machine Learning Data Engineer does. Familiarise yourself with data ingestion systems, ETL processes, and AWS infrastructure, as these are key components of the role.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, SQL, and any relevant data warehousing tools like Snowflake or Elasticsearch. Highlight specific projects where you've implemented data pipelines or managed databases.
✨Demonstrate Problem-Solving Abilities
Expect questions that assess your analytical thinking and problem-solving skills. Prepare examples of challenges you've faced in previous roles and how you overcame them, particularly in relation to data quality and processing.
✨Emphasise Collaboration
Since the role involves working closely with product teams and data scientists, be ready to discuss your experience in collaborative environments. Share examples of how you've successfully worked in teams to achieve common goals.