At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Job Description (Shaded areas for Talent use only) We are seeking a passionate data analyst to transform data into actionable insights and support decision-making in a global organization focused on pricing and commercial strategy. This role spans business analysis, requirements gathering, data modeling, solution design, and visualization using modern tools. The analyst will also maintain and improve existing analytics solutions, interpret complex datasets, and communicate findings clearly to both technical and non-technical audiences. Essential Functions of the Job: (Identify and describe essential functions, or primary duties and responsibilities. Each function should describe WHAT is done and the END RESULT/PURPOSE achieved. Assume that the reader does not know the role or function of the job.) Analyze and interpret structured and unstructured data using statistical and quantitative methods to generate actionable insights and ongoing reports. Design and implement data pipelines and processes for data cleaning, transformation, modeling, and visualization using tools such as Power BI, SQL, and Python. Collaborate with stakeholders to define requirments, prioritize business needs, and translate problems into analytical solutions. Develop, maintain, and enhance scalable analytics solutions and dashboards that support pricing strategy and commercial decision-making. Identify opportunities for process improvement and opperational efficiency through data-driven recommendations. Communicate complex findings in a clear, compelling, and actionable manner to both technical and non-technical audiences. Analytical/Decision Making Responsibilities: (Describe the kind of problems and challenges typically faced, and decisions required to perform the job, as well as recommendations made to supervisors or others. Focus on the nature of existing policies, precedents and procedures used to guide decisions, and the degree to which the incumbent is free to make decisions requiring interpretation and judgment. Provide an example.) Apply a hypothesis-driven approach to analyzing ambiguous or complex data and synthesizing insights to guide strategic decisions. Promote adoption of best practices in data analysis, modeling, and visualization, while tailoring approaches to meet the unique needs of each project. Tackle analytical challenges with creativity and rigor, balancing innovative thinking with practical problem-solving across varied business domains. Prioritize work based on business impact and deliver timely, high-quality results in fast-paced environments with evolving business needs. Demonstrate sound judgement in selecting methods, tools, and data sources to support business objectives. Knowledge and Skills Requirements: (Describe the knowledge or skills needed to perform this job; these may be professional, technical, or managerial) Proven experience as a data analyst, business analyst, data engineer, or similar role. Strong analytical skills with the ability to collect, organize, analyze, and present large datasets accurately. Foundational knowledge of statistics, including concepts like distributions, variance, and correlation. Skilled in documenting processes and presenting findings to both technical and non-technical audiences. Hands-on experience with Power BI for designing, developing, and maintaining analytics solutions. Proficient in both Python and SQL, with strong programming and scripting skills. Skilled in using Pandas, T-SQL, and Power Query M for querying, transforming, and cleaning data. Hands-on experience in data modeling for both transactional (OLTP) and analytical (OLAP) database systems. Strong visualization skills using Power BI and Python libraries such as Matplotlib and Seaborn. Experience with defining and designing KPIs and aligning data insights with business goals. Additional/Optional Knowledge and Skills: (Describe any additional knowledge or skills that, while not required, may be useful or helpful to perform this job; these may be professional, technical, or managerial) Experience with the Microsoft Fabric data analytics environment. Proficiency in using the Apache Spark distributed analytics engine, particularly via PySpark and Spark SQL. Exposure to implementing machine learning or AI solutions in a business context. Familiarity with Python machine learning libraries such as scikit-learn, XGBoost, PyTorch, or transformers. Experience with Power Platform tools (Power Apps, Power Automate, Dataverse, Copilot Studio, AI Builder). Knowledge of pricing, commercial strategy, or competitive intelligence. Experience with cloud-based data services, particularly in the Azure ecosystem (e.g., Azure Synapse Analytics or Azure Machine Learning). Supervision Responsibilities: (Describe the level of supervision received, i.e., the frequency of supervisory contact, degree to which the individual acts independently and on what kinds of issues. Describe the level of supervision of others, if any, i.e., assigning work, reviewing performance, direct or indirect responsibility). Operates with a high degree of independence and autonomy. Collaborates closesly with cross-functional teams including sales, pricing, and commercial strategy. Mentors junior team members, helping develop technical skills and business domain knowledge. Other Requirements: (Describe any miscellaneous functions or expectations of the job that are important to note) Collaborates with a team operating primarily in the Eastern Time Zone (UTC 4:00 / 5:00). Limited travel may be required for this role. Job Requirements: Education: (What is the minimum level of education or equivalent experience needed/suggested to perform this job effectively?) A bachelor’s degree in a STEM field relevant to data analysis, data engineering, or data science is required. Examples include (but are not limited to) computer science, statistics, data analytics, artificial intelligence, operations research, or econometrics. Experience: (What is the minimum number or range of years needed to perform this job?) 3–6 years of experience in data analysis, data engineering, or a closely related field, ideally within a professional services enviornment. Certification Requirements: (Describe and explain any certifications and/or licenses needed or helpful to perform this job). No certifications are required for this role. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.
