Data Engineering Manager - Apple Ads

At Apple, we work every day to create products that enrich people's lives. Our Advertising Platforms group enables people around the world to easily access informative and imaginative content on their devices, while helping publishers and developers promote and monetize their work. Our technology and services power advertising in Apple News and in Search Ads on the App Store. These platforms are highly performant, deployed at scale, and set new standards for delivering effective advertising while protecting user privacy. The Apple Ads data team is seeking a hands-on Data Engineering Manager to lead the design of large-scale data, analytics, and reporting systems. In this role, you will serve as both a technical contributor and a people leader, guiding a product-focused data engineering team while driving the development, execution, and continuous improvement of new and existing capabilities. You will directly enable teams of analysts, business users, and data scientists by establishing standards and frameworks that ensure the reliable and efficient production of new data products. You and your team will play a crucial role in building data products that uphold Apple's privacy commitments and transform how advertising works with data. You will collaborate closely with cross-functional teams to deliver actionable data insights that drive business strategies and decisions.

Description

As a Data Engineering Manager, you and your engineering team will guide the evolution of our data products to meet the challenges of increasing scale and complexity, ensuring that solutions are both reliable and performant. You will lead your team in delivering high-quality systems while staying actively engaged in technical problem-solving and design. A successful candidate will have a proven track record of leading or mentoring engineers and analysts, designing scalable data systems and products, and building data models for reporting and analytics. You will be well-versed in leveraging traditional ETL/ELT and data generation technologies, with a deep understanding of distributed data storage and processing frameworks such as Spark, Kafka, EMR, cloud data warehouses, and Hadoop.

Responsibilities

• Lead a group of engineers focused on rapidly developing, scaling, and supporting data products for online and offline consumption by business and analytic stakeholders, while remaining actively engaged in technical problem-solving and solution design.
• Define and incorporate best practices in storage, processing, and copy/synchronization appropriate for the scale and maturity of our data products.
• Collaborate with business stakeholders to understand needs, align priorities, and translate them into effective data solutions.
• Work closely with cross-functional teams to prototype new concepts and deliver end-to-end systems in an agile setting.
• Produce high-quality systems with strong reliability and scalability.
• Requires a strong understanding of the intersection between business, analytics, and engineering, resulting in a proactive approach that emphasizes reusable solutions to improve efficiency and time-to-insight.
• Must be able to work in a constantly evolving environment and perform optimally in a sprint-based, agile development setting.

• 3+ years of experience managing teams of software engineers and analysts
• Solid background in computer science, mathematics, or similar quantitative fields with a minimum of 8 years of professional experience
• Demonstrated expertise in practical applications of data warehousing concepts, methodologies, and frameworks using various distributed technologies such as SparkSQL, PySpark, Java, Scala, Hadoop, Kafka, Snowflake, and Vertica
• Proven ability to design for multiple cloud environments including Amazon Web Services, Microsoft Azure, and Google Cloud Platform
• Hands-on experience with SparkSQL, Hive, Druid, Cassandra, Solr, or other big data query engines
• Deep understanding of modern data architectures at scale, especially serving data as a platform in support of data science teams
• Advanced skills using one or more scripting languages (e.g., Python, Bash)
• Proficiency in applying data encryption and data security standards
• Expertise in modern table formats such as Apache Iceberg and file formats such as Parquet
• Knowledge of visualization tools such as Tableau and ThoughtSpot

• Demonstrated ability to communicate technical concepts to a business-focused audience
• Passion for reinforcing and enriching an engineering team culture that drives engagement and satisfaction
• Most importantly, a sense of humor and an eagerness to learn