Sr Data Engineer, AIML

Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists? Together, you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide!

The AI and Machine Learning team is looking for Senior Data Engineer to build world-class data infrastructure and solutions that are used by data scientists, ML engineers and researchers to power Apple Foundation Model lifecycle.

We are looking for a skilled Senior Data Engineer to join our engineering team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, data platforms, and data integration solutions that enable reliable analytics, machine learning, and business intelligence capabilities across the organization.

Design, build, and optimize large-scale batch and streaming data pipelines using Google Cloud Dataflow (Apache Beam) and related technologies. Develop and maintain data models and transformations in BigQuery to support analytics, reporting, and ML use cases. Build scalable and reliable data architectures using GCP services such as Pub/Sub, Dataflow, BigQuery, Cloud Storage, Dataproc, and Composer. Integrate data from multiple internal and external systems while ensuring data quality, consistency, lineage, and governance. Collaborate closely with data scientists, ML engineers, analysts, and product teams to deliver production-grade data solutions. Develop robust ETL/ELT workflows and orchestration pipelines using cloud-native tooling. Monitor, troubleshoot, and optimize data pipelines for performance, cost efficiency, and reliability. Implement best practices for data security, access control, and compliance in cloud environments.Strong software engineering foundation with minimum 10+ years of experience in data engineering, backend engineering, or distributed systems. Strong hands-on experience designing and building large-scale batch and streaming data pipelines using Python and distributed systems. Expertise in ETL/ELT development, event-driven architectures, and working with high-volume datasets using technologies such as Pub/Sub, Dataflow (Apache Beam), and Spark-based processing (Dataproc or equivalent). Deep expertise in the Google Cloud Platform data ecosystem, including BigQuery, Dataflow, Pub/Sub, and Composer (Airflow). Proven ability to design, optimize, and operate scalable data platforms, with strong experience in BigQuery performance tuning, cost optimization, data modeling, partitioning, clustering, and query optimization at scale. Proficient in Python, version control, CI/CD practices, testing, and code reviews. Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).

7+ years of experience in data engineering or large-scale distributed systems. Experience designing end-to-end data platforms or data mesh architectures. Strong understanding of data governance, data lineage, and metadata management frameworks. Experience supporting ML pipelines and MLOps workflows, including feature engineering and training data generation. Experience building systems with strong reliability, observability, and SLAs (monitoring, alerting, debugging distributed pipelines). Familiarity with Generative AI / LLM-based systems, including: LLM-powered data workflows, Agentic pipelines, Embedding/vector-based retrieval systems. Experience influencing architecture decisions across teams and driving technical direction. Experience collaborating in cross-functional, fast-paced tech environments or cloud-native organizations with a focus on building reliable, maintainable, and production-grade data systems.