AIML - Head of Data Engineering, Data & Machine Learning Innovation

Key Skills

Aartificial intelligence, Apache Flink, Data Analytics, Data Modeling, Data pipelines, Data Warehousing, Effective communication skills, Machine learning techniques, NoSQL, Presentation skills

Job Description

Are you passionate about data, data quality, efficiency, and scale? Would you like to play a critical part in accelerating data-informed product development to drive innovations and amazing user experiences? As the Head of Data Engineering, you and your team will be responsible for the crucial role of designing, operating, and supporting the increasingly complex environments that power modern data analytics and machine learning use cases for Apple products such as Siri, Dictation, Translations, Apple Search and others. You will partner closely with cross-functional teams and be responsible for the planning, execution, and success of technical projects as well as help drive scalable practices to accelerate the evolution of our products. You will lead existing data resources, build new datasets and data pipelines, and implement new technologies and tools to enable rigorous scalable science and analytics, and data-informed product development. Your team and vision will enable data to inform product engineering teams at scale, with the ultimate purpose of improving the product experience for Apple users across hardware, software, and services, while preserving Apple’s privacy values. You will drive a strong culture of data excellence!

Key Qualifications

• 10 years in a managerial role, growing and scaling organizations
• Experience in multiple large-scale data processing systems, data telemetry, data-driven performance and reliability improvement across platforms and products, the data foundation for advanced machine learning problems, and data/analytics.
• Demonstrated passion and leadership in data engineering, data-informed product development, building and shaping data culture
• Proven ability to proactively drive a data engineering agenda and prioritize work based on impact and strategic investment
• Experience with modern data engineering stack, processing technologies, data, and analytic pipelines, including batch (Spark, others), streaming systems (Flink, etc.), and scalable query engines (e.g. Trino, Druid, Pinot, etc.)
• Solid understanding of both relational and NoSQL database technologies and experience in engineering datasets and metrics out of massive and complex data
• Expertise in data engineering partnering with analytics and ML teams (comprised of data scientists and statisticians) to enable horizontal and vertical use cases
• Demonstrated ability to establish strong partnerships and drive processes across organizations, e.g. to manage the creation, processing, and use of instrumentation to enable using data impactfully in developing products
• Outstanding communication and presentation skills, written and verbal, to all levels of an organization

Description

In this role you will build upon the above essential qualifications and:

• Proactively drive the vision for Data Warehouse, Data Platform, Analytics, and ML analytics to accelerate the building and improvement of our products, and define and execute a plan to achieve that vision.
• Grow and scale a large, established data engineering team and build strong relationships with partner teams
• Drive the design, building, and launching of new data models and data pipelines in production
• Stay focused and prioritize a heavy workload while achieving exceptional quality and driving long-term vision

Education & Experience

Bachelors or Master in CS, Engineering, Math, Statistics, or a related field, or equivalent practical experience in engineering

Pay & Benefits

• At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $242,700 and $400,700, and your base pay will depend on your skills, qualifications, experience, and location.