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The Analytics Platforms & Experiences (APX) team is at the forefront of revolutionising Data Engineering. We drive significant increases in efficiency and productivity through a flawless ecosystem of frameworks and products that unlock observability, knowledge and enable data quality-driven orchestration at scale. APX is part of the broader Apple Services Engineering division that powers App Store, Apple TV+, Apple Music, Apple Podcasts, Apple Books, Fitness+, the i Tunes Store and more.

The APX Bedrock team is the foundational platform layer within APX, responsible for building the systems and services that underpin data engineering excellence across the organization. We are seeking a Senior Software Data Engineer who brings deep technical expertise and the strategic thinking to shape the future of our data engineering platform. In this role, you will influence the broader technical vision, drive platform-wide architectural decisions, and establish patterns that raise the engineering bar across the organization. You will bridge hands-on engineering excellence with forward-looking platform thinking, working alongside architects and cross-functional teams to transform how data engineers work at Apple.

Description

As a Senior Software Data Engineer on the APX Bedrock team, you will be a highly impactful technical contributor who combines deep engineering craftsmanship with platform-wide thinking. You will own critical platform components end-to-end while actively shaping the technical direction of the team and influencing the broader organization.

Lead design and implementation of core platform services supporting data application authoring, metadata management, dataset lifecycle tracking, and orchestration at scale

Architect and build high-performance Java-based backend services and APIs that form the backbone of the data engineering platform

Own data modeling and database design decisions, establishing best practices for schema design, indexing strategies, and query optimization across relational and non-relational data stores

Design and operate scalable data processing pipelines using Apache Spark, Apache Flink, and modern orchestration frameworks

Proactively identify architectural gaps and drive initiatives that improve platform scalability, reliability, and developer experience

Define engineering standards and patterns adopted across the broader team and organization

Evaluate emerging technologies and contribute to strategic decisions that shape the platform roadmap

Mentor engineers across experience levels and drive a culture of technical excellence

Leverage AI-powered development tools and help define how AI-augmented workflows are adopted and scaled within the team

Preferred Qualifications

Experience with non-relational or NoSQL databases and strong intuition for applying the right data storage paradigms

Proficiency in Scala, Go, or Python as complementary programming languages

Deep experience with container orchestration tools such as Kubernetes and cloud-native infrastructure patterns

Hands-on experience with AI-powered development tools (e.g., Claude Code, Copilot) and building AI-augmented engineering workflows

Experience architecting data quality monitoring, metadata management, or dataset lifecycle management systems at scale

Strong background in streaming, real-time data processing, and event-driven architectures

Track record of mentoring senior engineers and driving technical excellence across teams

Strong communication skills with the ability to present complex technical strategies to senior leadership

Minimum Qualifications

Bachelor's or Master's Degree in Computer Science, Engineering, or equivalent professional experience

8+ years of software engineering experience with significant focus on data platform or large-scale distributed systems

Expert-level Java programming with a strong track record of architecting and delivering complex, production-grade backend services

Deep experience with relational databases including advanced data modeling, schema design, and query optimization at scale

Expert-level experience with Apache Spark and Apache Flink, and distributed data technologies including Hadoop, HDFS, and Kafka

Extensive experience designing and operating workflow orchestration systems such as Apache Airflow at enterprise scale

Proven ability to drive end-to-end technical ownership of complex platform initiatives and influence technical direction beyond immediate team scope

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .

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 $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.