Full-time
Posted 20 days ago
Job description
About the job Remote Data Analyst - Entry Level Data Analyst - Workplace Experience We entertain millions of people across the globe with the most amazing and immersive interactive software in the industry. But making games is hard. That's why we employ the most creative, passionate people in the industry.
The Challenge Ahead The Workplaces team oversees workplace environments and the related employee experience globally and supports and maintains spaces that are vibrant, fresh, branded, and inspiring. The team also provides relevant programs that differentiate us as an employer of choice and support EA's ability to attract and retain the talent needed to power our Company. Workplace Experience Is Focused On Services And Programs That Directly impact employee effectiveness Provide a healthy physical office environment Create a consistent physical setting and workplace experience globally while reflecting local culture Build community and connectedness among our people Directly help foster our values Workplace Experience is going through an exciting time as we redefine the future of EA's workforce and implement our new ways of working.
As a Data Analyst you will focus on discovering trends, identifying risks and constraints, providing recommendations, being an advisor to the WE senior leadership team. You will apply your modeling skills to develop forecasts and insights to improve existing workplaces to create an amazing employee experience as we evolve our future workplace model. You will wrangle data across multiple data sources, apply the right models, and visualize insights for senior leaders and partners.
You'll also learn new systems, tools, and bring industry best practices to analyze big data and enhance our workplace analytics. You will report to the Senior Manager for the Workplace Experience Program Management Office. Key Responsibilities Communicate complex analysis and concepts and provide readable dashboards to surface relevant insights with clear narratives to senior leadership and non-technical partners Develop forecasting models to understand EA's future ways of working and the evolution of our workforce - focusing on headcount predictions, office attendance and other human capital analyses Work with program managers and leadership to determine business problems and use statistical analysis, simulations, predictive modeling, or other methods to analyze and develop practical solutions Advocate for the importance of data-driven decision-making and build relationships with partners who rely on people data to ensure understanding on needs, definition of metrics and ways of working Champion for data quality, creating policy and implementing monitoring solutions to ensure compliance Perform research, assemble and integrate data from multiple sources, conduct analyses, design and implement analytical solutions Identify and advocate for technical options related to machine learning, data mining, and other statistical approaches.
Maintain data repositories, interfaces and protocols What We're Looking For Minimum of bachelor's Degree in Data Science, Applied Mathematics, Architecture or Engineering with an emphasis on quantitative methods 3+ years in a past data analyst role (or similar) that involved thinking of big pictures, suggesting changes to process, identifying risks and potential opportunities for data improvement Communicate with non-technical partners Data Analysis- spreadsheet mastery including VBA, PivotTables, and array functions - or similar tools used for discovering data patterns and correlations through statistical methods Manage and facilitate collaborations with global partners Customer-centric skills - able to engage with business and IT partners to discover requirements for Business Intelligence solutions Storytelling and project management skills - direct experience with data visualization tools such as Power BI or Tableau. Understanding of network and data privacy requirements with knowledge of integrity and security tools Experience with Workday, Visier, CCure, and FMI a plus Experience working with human capital data #J-18808-Ljbffr
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Frequently asked questions
Q: What skills or qualities help someone succeed as a Data Analyst?
A: To succeed as a Data Analyst, key technical skills include proficiency in programming languages such as Python or R, expertise in data visualization tools like Tableau or Power BI, and knowledge of statistical analysis and machine learning concepts. Additionally, strong soft skills like effective communication, problem-solving, and collaboration are crucial for presenting insights to stakeholders and working with cross-functional teams. By combining these technical and soft skills, Data Analysts can drive business decisions, identify areas for improvement, and contribute to the growth and success of their organization.
Q: What is the career path for a Data Analyst?
A: A Data Analyst's typical career progression involves starting as an Entry-Level Data Analyst, where they collect, analyze, and interpret data to inform business decisions. As they gain experience, they can move into Mid-Level roles such as Senior Data Analyst or Business Analyst, where they take on more complex projects and lead smaller teams. Ultimately, they can advance to Senior Leadership positions like Data Scientist, Data Manager, or even Director of Analytics, where they oversee large-scale data initiatives and drive strategic business growth.\n\nKey opportunities for skill development and professional growth in this role include learning programming languages like Python or R, mastering data visualization tools like Tableau or Power BI, and staying up-to-date with emerging trends in machine learning and artificial intelligence. Additionally, Data Analysts can develop soft skills like communication, project management, and leadership to excel in their roles.\n\nLong-term career prospects for Data Analysts are diverse, with potential directions including transitioning into related fields like Business Intelligence, Data Engineering, or even becoming a Product Manager, or pursuing advanced degrees in Data Science or related fields to further specialize in areas like machine learning or data engineering.