Engineering Program Manager

Apple • Cupertino, CA

1d

About The Position

Apple’s Sales organization generates the revenue needed to fuel our ongoing development of products and services. This, in turn, enriches the lives of hundreds of millions of people around the world. We are, in many ways, the face of Apple to our largest customers. Apple's US Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating AI solutions that have a direct and measurable impact on Apple Sales and its customers. We’re looking for an Engineering Program Manager with strong execution, communication, and technical program management skills to help scale AI-enabled insights generation and data operations. You’ll be responsible for coordinating cross-functional work across engineering, data science, analytics, and business teams to ensure insights are accurate, actionable, operationally reliable, and embedded into real-world sales workflows.

Requirements

• 8+ years of experience in engineering program management, data program management, analytics operations, or a related role supporting data, analytics, AI, or enterprise software teams.
• Hands-on experience in data science or data engineering, where you’ve shipped pipelines, models or production analytics.
• Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.
• Strong understanding of data operations workflows, including data ingestion, data quality checks, business logic validation, reporting cycles, issue triage, and operational support.
• Strong ability to manage ambiguity, clarify scope, document decisions, align owners, and drive execution across multiple teams and priorities.
• Experience with AI-enabled analytics or insights workflows, including LLM-generated summaries, automated insights, data narratives, quality review, and human-in-the-loop validation.
• Strong attention to detail and ability to identify risks related to data quality, logic gaps, unclear ownership, missed dependencies, stakeholder alignment, or delivery readiness.
• Strong communication skills, with the ability to explain data, process, and technical topics clearly to both technical and non-technical audiences.
• Ability to work in a fast-paced, dynamic, constantly evolving business environment.
• Ability to manage multiple workstreams in a fast-paced environment while balancing planned roadmap work, operational issues, and ad hoc business requests.
• B.S. degree in Engineering, Computer Science, Data Science, Information Systems, or a related field, or equivalent practical experience.

Nice To Haves

• Familiarity with modern AI/GenAI capabilities for insights generation, including automated summaries, natural-language explanations, anomaly detection, recommendations, and agentic workflows.
• Experience working with tools such as Jira, Confluence, Tableau, GitHub, Airflow, dbt, Snowflake, Spark, Databricks, or similar data and engineering platforms.
• Understanding of operational risks in data and AI products, including stale data, broken pipelines, metric inconsistencies, model behavior issues, permission gaps, and unclear ownership.
• Experience working with globally distributed engineering or data teams and coordinating execution across multiple time zones.
• Strong judgment on when to escalate technical risks, adjust scope, pause delivery, or align leadership on tradeoffs.
• Advanced Degree (MS or Ph.D.) in Computer Science, Engineering, Data Science, Information Systems, Statistics, Business Analytics, Operations Research, or a related quantitative/technical field is preferred.

Responsibilities

• Coordinating cross-functional work across engineering, data science, analytics, and business teams to ensure insights are accurate, actionable, operationally reliable, and embedded into real-world sales workflows.
• Driving cross-functional data or analytics programs from planning through execution, including roadmap tracking, milestone management, dependency coordination, risk management, and stakeholder communication.
• Partnering with engineering, data science, analytics, product, business operations, and sales operations teams to translate business needs into clear execution plans.
• Managing ambiguity, clarifying scope, documenting decisions, aligning owners, and driving execution across multiple teams and priorities.
• Managing multiple workstreams in a fast-paced environment while balancing planned roadmap work, operational issues, and ad hoc business requests.
• Partnering with engineering teams on technical readiness across data pipelines, APIs, model integrations, monitoring, data refreshes, access controls, and launch criteria.
• Coordinating execution across multiple time zones.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree