Support Operations Data Analyst, Employee Experience & Productivity, IS&T

Summary

The difference between data and intelligence is context. This role enriches data with business context to produce insights that enable intelligent decisions. Join Apple's Information Systems and Technology (IS&T) organization, the engine powering Apple. As a member of the Employee Productivity & Support Data Science team, you will use data to improve how Apple delivers technical support to its employees.

You will pull and prepare data from multiple systems, build dashboards and visualizations, and conduct analyses that help IT support leaders understand operational performance and make informed decisions. This role is about interpreting data, extracting insights, and partnering closely with the business to turn those insights into better decisions.

About the role

In this role, you will support the IS&T Support organization by building and maintaining the analytical assets it relies on — dashboards, reports, and ad-hoc analyses that surface how the support ecosystem is performing across a variety of support channels.

You will draw on your expertise in IT support analytics to understand how employees seek help, identify where processes can improve, and develop metrics that measure operational efficiency and service quality.

A core part of the role is partnership: you will work directly with support leaders and operational managers to understand their challenges, translate them into analytical work, and deliver findings that are clear, accurate, and actionable. You are not building in isolation — you are a thought partner who understands the business well enough to know what to measure and why it matters.

Day to day, you will write SQL to model data from large datasets, build and maintain Tableau dashboards, conduct analyses to identify trends and anomalies, and present your findings to stakeholders at various levels — all while using AI tools to accelerate your analytical work. The ideal candidate is someone who is technically strong, detail-oriented, and equally motivated by the work the data enables as by the data itself.

Responsibilities

• Partner with support leaders and operational managers to understand their needs, translate business questions into data problems, and deliver recommendations grounded in evidence.
• Extract, clean, transform, and validate data from multiple support systems — including ticketing platforms, contact center tools, and AI interaction logs — creating reliable datasets for analysis and visualization.
• Build and maintain dashboards and reports in Tableau, and actively consume those assets to identify trends, surface anomalies, and communicate insights back to stakeholders.
• Conduct deep-dive analyses on support operations data to answer questions that existing dashboards do not address — packaging findings into clear, audience-ready deliverables such as slides or annotated visualizations.
• Use AI to accelerate insight generation — summarizing patterns, classifying data, and enhancing the speed and depth of analytical work — while validating outputs to ensure accuracy and reliability.
• Develop and maintain well-documented, reproducible analytical workflows — writing clean SQL, organizing code in GitHub, and ensuring that analyses can be understood and extended by others on the team.
• Monitor data quality across support data sources, flagging issues proactively and working with data engineering to resolve them.

Minimum Qualifications

• 3+ years of experience working with IT support data, including ticketing systems, contact center platforms, or multi-channel interaction data.
• 3+ years of hands-on experience writing SQL to extract, transform, and analyze data from large, multi-source datasets.
• 3+ years of experience building dashboards and visualizations in Tableau, Looker, or equivalent.
• 1+ years of experience in Python or R for data manipulation and analysis.
• 3+ years of experience delivering analytical work in a fast-paced environment with evolving priorities and multiple concurrent stakeholders.
• Bachelor's degree and 3+ years of relevant experience in data analytics, business intelligence, data science, or a related quantitative field — or a Master's degree in a quantitative or business discipline with 2+ years of relevant experience.

Preferred Qualifications

• Master's degree or PhD in a quantitative or business field (e.g., statistics, data science, economics, operations research, applied mathematics, the natural sciences, or an MBA with analytics concentration)
• Working knowledge of support operations and how data informs staffing models, triage optimization, and process improvement.
• Working knowledge of clickstream data and web analytics as applied to content or service performance measurement.
• Proficiency in version control and collaborative documentation practices using tools like GitHub.
• Knowledge of statistical modeling, including hypothesis testing, regression, and foundational causal inference methods.
• Track record of applying AI to real-world data analytics and general productivity challenges.
• Ability to communicate analytical findings clearly to both technical and non-technical audiences, adapting depth and format to the situation.
• Comfort with ambiguity; ability to define the analytical approach when the business question is loosely framed.
• Proven ability to build trust with leaders across diverse functional areas.