Lead Big Data Engineer - Wallet Commerce

Austin, Texas, United States Software and Services

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better.

It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something!

Apple Pay brought mobile payment to millions of customers, and it’s just the beginning! We are looking for engineers who enjoy both hands-on technical work and designing thoughtful, scalable services for Wallet and Apple Pay. Our team’s vision is to be the engine of intelligent transformation, leveraging a unified, reliable data platform to build and deploy innovative and solutions that drive significant business impact and enable data-driven decision-making throughout the organization.

We are seeking a pragmatic Senior Big Data Engineer to help build and optimize data, analytics, and ML enablement solutions. You will design and deliver privacy-first, reliable, and quality-focused data products and platforms that enable trusted, compliant data usage across the organization. You will collaborate with cross-functional teams across time zones to ensure our data platforms meet the highest standards of trust, accuracy, and regulatory compliance.

• Instrument APIs, user journey, and interaction flows to systematically collect behavioral, transactional, and operational data, enabling robust analytics and insightful reporting.
• Design, develop, and maintain scalable data & ML pipelines and architectures for Wallet, Payments & Commerce products.
• Optimize data workflows and pipelines to enhance data processing efficiency and reliability.
• Design and implement automated data quality frameworks (data validation, continuous data quality, data profiling, anomaly detection, reconciliation, etc.).
• Develop and scale QA automation frameworks for data engineers, including reusable test suites for schema validation, pipeline regression, and performance benchmarking.
• Uphold the highest standards for user privacy, ensuring all data engineering practices and designs embed privacy by default and by design.
• Work closely with legal and compliance teams to anticipate and ensure continuous adherence to regulations and industry-specific mandates
• Collaborate closely with a diverse set of partners to gather requirements, prioritize use cases, and ensure high-quality data products delivery.

• Bachelor’s degree in Computer Science or a related technical field or equivalent experience
• 5-10 years of experience in designing, developing, and deploying data engineering for analytics or ML & AI pipelines.
• Expertise with data governance, security protocols, and compliance in financial data systems.
• Strong proficiency in SQL, Scala, Python, or Java, with hands-on experience in data pipeline tools (e.g., Apache Spark, Kafka, Airflow), CI/CD practices, and version control.
• Familiarity with cloud platforms (AWS, Azure, GCP) and data management and analytics tools like Snowflake, Databricks and Tableau.
• Strong understanding of data warehousing, data modeling (dimensional/star schemas), and metric standardization.
• Proven experience building data quality frameworks (validation, profiling, anomaly detection, synthetic data generation).
• In-depth knowledge of privacy-preserving techniques and compliance in financial systems.
• Excellent problem-solving and analytical skills, with the ability to influence stakeholders and drive adoption of best practices.

• Experience authoring technical and instrumentation specs, and working with APIs and message schemas (MSDs).
• Proven ability to design reusable frameworks, tools, and automation to accelerate platform adoption.
• Hands-on experience with distributed querying (Trino),…