Engineering Program Manager

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 theres no telling what you could 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 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.

Were 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. Youll 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.

Own the execution plan for insights generation and data operations workstreams ensuring priorities timelines owners dependencies and risks are clearly the end-to-end delivery cycle for insights from business requirement gathering to data readiness QA stakeholder review publishing and post-launch establish repeatable operating processes for how insights are requested built validated released monitored and improved over and unblock dependencies across data sources pipelines semantic layers AI-generated outputs and downstream business launch readiness by making sure data quality business logic access documentation support plans and stakeholder communications are in status risks tradeoffs and decisions clearly to engineering leads business partners and senior leadership.

8 years of experience in engineering program management data program management analytics operations or a related role supporting data analytics AI or enterprise software -on experience in data science or data engineering where youve shipped pipelines models or production looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive driving cross-functional data or analytics programs from planning through execution including roadmap tracking milestone management dependency coordination risk management and stakeholder understanding of data operations workflows including data ingestion data quality checks business logic validation reporting cycles issue triage and operational to partner with engineering data science analytics product business operations and sales operations teams to translate business needs into clear execution ability to manage ambiguity clarify scope document decisions align owners and drive execution across multiple teams and with AI-enabled analytics or insights workflows including LLM-generated summaries automated insights data narratives quality review and human-in-the-loop attention to detail and ability to identify risks related to data quality logic gaps unclear ownership missed dependencies stakeholder alignment or delivery communication skills with the ability to explain data process and technical topics clearly to both technical and non-technical to work in a fast-paced dynamic constantly evolving business to manage multiple workstreams in a fast-paced environment while balancing planned roadmap work operational issues and ad hoc business .S. degree in Engineering Computer Science Data Science Information Systems or a related field or equivalent practical experience.

Familiarity with modern AI/GenAI capabilities for insights generation including automated summaries natural-language explanations anomaly detection recommendations and agentic working with tools such as Jira Confluence Tableau GitHub Airflow dbt Snowflake Spark Databricks or similar data and engineering to partner with engineering teams on technical readiness across data pipelines APIs model integrations monitoring data refreshes access controls and launch of operational risks in data and AI products including stale data broken pipelines metric inconsistencies model behavior issues permission gaps and unclear working with globally distributed engineering or data teams and coordinating execution across multiple time judgment on when to escalate technical risks adjust scope pause delivery or align leadership on 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.