Data Analytics / MPP Engineer

The people here at Apple don’t just build products - they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that encourages the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. We are looking for passionate and dedicated professionals to join our Data Services’ Data Analytics and Engineering team. This team manages various state of the art open source technologies in Big Data Analytics and Gen-AI areas, including Hadoop ecosystem, AI/ML and Vector Databases as well as Enterprise MPP systems. The ideal candidate should have experience in architecting/deploying large-scale Analytics systems and services, ready to learn and explore new leading edge technologies.

Description

Are you passionate about building highly scalable, distributed applications that efficiently handle large volumes of data? Do you thrive on mastering various Big Data and MPP technologies and selecting the best ones for each application? If you are excited by the challenge of working in a fast-paced environment where your technical skills are tested daily and you want your work to impact millions of Apple users, we want to hear from you. In this role, you will join Apple's Data Services Data Analytics and Engineering team as a highly motivated, detail-oriented, and energetic individual with excellent communication skills. Your responsibilities will include: Design, architect, engineer and mentor in Big Data and MPP technologies, platforms and applications. Lead the design, development and management of new solutions using the latest technologies and data platforms such as SingleStore, Vertica, Clickhouse, Doris across various environments, including physical & virtual machines, containers, and cloud platforms. Develop scalable, robust systems that adapt to evolving business requirements. Measure, Compare, Optimise and Tune the System / Datastore performance Create proofs of concept for emerging technologies, performance benchmarking and architect scalable applications. Recommend and implement tools and processes to accelerate the development, deployment and maintenance of Analytics workloads. Collaborate with application owners, developers, and project managers, leveraging your expertise to provide guidance on architectural decisions and address complex technical challenges related to scaling and performance tuning.

Minimum Qualifications

• Strong educational background in Computer Science or Software Engineering.
• 8 to 12 years of professional experience managing complex, distributed and multi-tier Big Data / Analytics environments.
• Proven experience as a database SRE, specifically with MPP technologies such as SingleStore, ClickHouse, Vertica, Doris, etc
• In-depth knowledge of datastore performance tuning, query optimisation and troubleshooting along with DR setup / Backup and recovery procedures.
• Extensive experience working with public cloud platforms (AWS & GCP) and Kubernetes container deployments.
• Expertise in automation, including the development of tools and scripts to monitor server-side components and improve operational efficiency.
• Familiarity with architectural and design considerations related to scalability, reusability, and flexibility.
• Demonstrate strong problem-solving and critical thinking skills, along with a solid ability to quickly evaluate and apply new technologies.
• A passion for solving challenging engineering problems and triaging complex operational issues in collaboration with various engineering and customer teams.

Preferred Qualifications

• Experience with data visualisation tools such as Tableau, Looker and Apache Superset.
• Knowledge of open-source Vector databases such as Milvus, Weaviate, etc. is a plus.
Proficient in Application development using any programming language, with a focus on designing and building tools and frameworks for managing and scaling Big Data technologies.

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