This role pays more than
80%
of similar roles. Most pay
$126,800–$208,625
— the shaded band above.
At the midpoint, this role pays about
$214k
versus about
$168k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
2254 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1841 roles with salary data.
The Data Governance Engineer joins the Apple Ads Data Governance team to develop privacy-centric advertising solutions and manage data governance objectives. This role involves collaborating with cross-functional teams to implement critical privacy safeguards, data management controls, and automated policy enforcement for petabyte-scale pipelines. You will build autonomous governance agents, MCP integrations for PII detection, and intelligent monitoring systems that detect anomalies and auto-remediate issues. The work focuses on the technical challenge of ensuring compliance, quality, and security within large-scale advertising data environments. Key technologies include Python, Java, Rust, Spark, Kafka, Hadoop, and Flink. You will also utilize tools such as Docker, Kubernetes, Terraform, Prometheus, Grafana, and Datadog. Additionally, you will work with advanced frameworks like LangChain or AutoGen to develop multi-agent AI systems while managing data across platforms like Snowflake and Databricks.
Design autonomous governance agents for petabyte-scale data pipelines using Kafka, Spark, and Hadoop.
Build MCP integrations to automate policy enforcement, PII detection, and data quality validation.
Develop intelligent monitoring systems to detect anomalies, generate compliance reports, and auto-remediate issues.
Create reusable agent templates and workflows that adapt to changing regulations while maintaining performance.
Contribute to a distributed agent ecosystem featuring self-healing mechanisms and automated failovers for scalability.
Utilize AI coding agents to accelerate implementation, testing, and debugging while ensuring privacy and cost efficiency.
What we're looking for
B.S. in Computer Science or related field with 5+ years of software development experience, including exposure to ML/AI applications.
Production experience in Python, Java, Rust, or similar languages for data processing and API development.
Understanding of distributed systems, cloud platforms, CAP theorem tradeoffs, and basic ML model deployment.
Experience with containerization (Docker), orchestration (Kubernetes), and infrastructure as code tools like Terraform or CloudFormation.
Proficiency in CI/CD pipelines, DevOps practices, and automated testing using Git, GitHub Actions, Jenkins, or GitLab CI.
Familiarity with observability tools such as Prometheus, Grafana, or DataDog and logging frameworks for production systems.
Basic knowledge of machine learning concepts, MLOps, data pipelines, model versioning, and experiment tracking tools.
Expertise in open source data analytics platforms (Datahub, Spark, Flink, Hive, Hadoop/HDFS, Iceberg), multi-agent AI systems (LangChain, LangGraph, AutoGen), and modern data platforms (Snowflake, Databricks) (preferred).