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- Tasks: Join us to build impactful analytics platforms and data pipelines for Apple.
- Company: Be part of Apple's innovative team, shaping customer experiences through data.
- Benefits: Enjoy flexible work options, competitive pay, and a culture of creativity.
- Why this job: Work on cross-functional projects that drive meaningful change and innovation.
- Qualifications: Bachelor's in Computer Science or equivalent, plus 2+ years in large-scale systems.
- Other info: Ideal for those passionate about quality code and real-time data processing.
The predicted salary is between 43200 - 72000 £ per year.
The ASE Analytics & Data Engineering team is responsible for building analytics platforms, datasets and processes required by Apple for analysing and powering customer experiences. This means we build computation platforms and datasets to empower our product, marketing, feature, analytic and data science teams. Given the size and complexity of our datasets, this is not a trivial task. We are looking for an outstanding Software Engineer who can effectively collaborate with our partner teams to deliver data engineering solutions to improve and power the next generation of Apple features.
You will be working on cross-functional projects with other engineering teams, product leads and analytics leaders to build insights, metrics and data pipelines. The projects you will be working on will be truly impactful. You will have the freedom to innovate as you work closely with our partners to drive meaningful change and build elegant systems to deliver the results.
The ideal candidate will have a strong quality focus and be motivated by taking early production systems and developing them into services which can be run reliably at scale. Our systems are large scale and highly distributed in nature and you will be able to reason about complex failure modes which could occur. Attention to detail and perseverance to deliver high-quality, well tested and maintainable code, is a must.
Minimum Qualifications- Bachelor’s degree in Computer Science, Software Engineering or equivalent experience
- 2+ years of experience designing and developing large-scale distributed systems
- Proficiency in Python, Java, or Scala for big data processing
- Experience with modern data processing and warehousing technologies
- Excellent written and verbal communication skills for collaborating across distributed teams
- Practical experience of streaming technologies like Kafka or Flink
- Experience building and maintaining real-time data pipelines
- Strong background in software testing methodologies and practices
- Proficiency in rapidly prototyping and developing proof of concept systems to validate new ideas
Software Engineer, Analytics & Data Engineering employer: Job Traffic
Contact Detail:
Job Traffic Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer, Analytics & Data Engineering
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Python, Java, Scala, and streaming technologies like Kafka or Flink. Having hands-on experience or projects showcasing these skills can set you apart during discussions.
✨Tip Number 2
Network with current or former employees of the ASE Analytics & Data Engineering team at Apple. Engaging in conversations about their experiences can provide valuable insights into the team culture and expectations, which you can leverage during your interviews.
✨Tip Number 3
Prepare to discuss your experience with large-scale distributed systems and how you've tackled complex failure modes in past projects. Be ready to share specific examples that demonstrate your problem-solving skills and attention to detail.
✨Tip Number 4
Showcase your ability to collaborate across teams by preparing examples of successful cross-functional projects you've worked on. Highlight how you communicated effectively with different stakeholders to achieve project goals, as this is crucial for the role.
We think you need these skills to ace Software Engineer, Analytics & Data Engineering
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in software engineering, particularly with large-scale distributed systems. Emphasise your proficiency in Python, Java, or Scala, and any experience with data processing technologies.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about analytics and data engineering. Mention specific projects where you've collaborated with cross-functional teams and how you’ve contributed to building impactful data solutions.
Showcase Your Technical Skills: Include specific examples of your work with streaming technologies like Kafka or Flink. Discuss any real-time data pipelines you've built and how they improved processes or outcomes in previous roles.
Highlight Communication Skills: Since the role requires collaboration across distributed teams, emphasise your written and verbal communication skills. Provide examples of how you've effectively communicated complex technical concepts to non-technical stakeholders.
How to prepare for a job interview at Job Traffic
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, Java, or Scala in detail. Bring examples of large-scale distributed systems you've worked on and be ready to explain the challenges you faced and how you overcame them.
✨Demonstrate Collaboration Abilities
Since the role involves working with cross-functional teams, highlight your communication skills. Share specific instances where you successfully collaborated with product leads or analytics teams to deliver impactful projects.
✨Emphasise Quality and Testing
Discuss your approach to software testing methodologies. Be ready to talk about how you ensure high-quality, maintainable code and any experiences you have with building reliable systems at scale.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to reason about complex failure modes in distributed systems. Practice articulating your thought process when tackling technical problems, as this will demonstrate your analytical skills.
