Lead Big Data Engineer - Wallet, Payments & Commerce at Apple

Austin, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

29 Dec, 25

Salary

0.0

Posted On

30 Sep, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Analytics, Machine Learning, Data Governance, Security Protocols, SQL, Scala, Python, Java, Data Pipeline Tools, Cloud Platforms, Data Warehousing, Data Modeling, Data Quality Frameworks, Privacy-Preserving Techniques, Problem-Solving

Industry

Computers and Electronics Manufacturing

Description

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. DESCRIPTION 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. MINIMUM QUALIFICATIONS 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. PREFERRED QUALIFICATIONS 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), real-time analytics (OLAP), near-real-time processing (NRT), and data mesh architectures. Familiarity with Generative AI/LLM applications for test generation, anomaly detection, documentation automation, and privacy-safe synthetic data creation. Demonstrated ability to mentor engineers in data quality, privacy, and compliance best practices. Experience in Fintech, Wallet, Payments, or Digital Commerce domains, including regulatory considerations. Track record of independent problem-solving, sound technical judgment, and delivering impactful results at scale.

Responsibilities

Design and deliver privacy-first, reliable, and quality-focused data products and platforms. Collaborate with cross-functional teams to ensure data platforms meet high standards of trust, accuracy, and regulatory compliance.