Engineering Program Manager

Apple • Cupertino, CA

6h

About The Position

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.
• Experience driving cross-functional data or analytics programs from planning through execution, including roadmap tracking, milestone management, dependency coordination, risk management, and stakeholder communication.
• Strong understanding of data operations workflows, including data ingestion, data quality checks, business logic validation, reporting cycles, issue triage, and operational support.
• Ability to partner with engineering, data science, analytics, product, business operations, and sales operations teams to translate business needs into clear execution plans.
• 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.
• B.S. degree in Engineering, Computer Science, Data Science, Information Systems, or a related field, or equivalent practical experience.

Nice To Haves

• Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.
• 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

• Own the execution plan for insights generation and data operations workstreams, ensuring priorities, timelines, owners, dependencies, and risks are clearly managed.
• Coordinate the end-to-end delivery cycle for insights, from business requirement gathering to data readiness, QA, stakeholder review, publishing, and post-launch monitoring.
• Help establish repeatable operating processes for how insights are requested, built, validated, released, monitored, and improved over time.
• Track and unblock dependencies across data sources, pipelines, semantic layers, AI-generated outputs, and downstream business workflows.
• Support launch readiness by making sure data quality, business logic, access, documentation, support plans, and stakeholder communications are in place.
• Communicate status, risks, tradeoffs, and decisions clearly to engineering leads, business partners, and senior leadership.
• Partner with engineering teams on technical readiness across data pipelines, APIs, model integrations, monitoring, data refreshes, access controls, and launch criteria.
• Manage multiple workstreams in a fast-paced environment while balancing planned roadmap work, operational issues, and ad hoc business requests.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior