Engineering Program Manager
Cupertino, California, United States of America
Contract
4 weeks ago
Key skills
AIGenAILLMAgenticData EngineeringAnalyticsTableauSnowflakeDatabricksSparkAirflowdbtGitHubJiraConfluenceLeadershipRisk ManagementCommunicationSales
About this role
Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there's no telling what you could accomplish.
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.
Description
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.
Minimum Qualifications
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.
We're looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.
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.
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.
Preferred Qualifications
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.
Ability to partner with engineering teams on technical readiness across data pipelines, APIs, model integrations, monitoring, data refreshes, access controls, and launch criteria.
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.
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.
Description
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.
Minimum Qualifications
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.
We're looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.
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.
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.
Preferred Qualifications
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.
Ability to partner with engineering teams on technical readiness across data pipelines, APIs, model integrations, monitoring, data refreshes, access controls, and launch criteria.
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.