Principal Data Analyst

Walmart•Bentonville, AR

1d•Onsite

About The Position

The Principal Data Analyst role involves understanding business context to evaluate proposed business cases, translating business requirements into strategies, and driving the execution of deliverables with demonstrable value. Key responsibilities include identifying suitable data sources, performing initial data quality checks, and recommending appropriate data visualization tools. The analyst will generate graphical representations, design data representations for complex datasets, and build compelling stories to present to diverse audiences, influencing behavioral change through clear communication. The position also focuses on Data Quality Management (DQM), including developing awareness, determining strategy, metrics, and business rules, designing and implementing DQM procedures, and leading initiatives to improve business performance. Furthermore, the role requires promoting Knowledge Discovery in Data (KDD), applying suitable KDD tools, staying updated on best practices, testing and evaluating solutions, establishing statistical data standards, conducting statistical experiments, building statistical models using tools like SAS, and publishing research documents. The Principal Data Analyst will consult on complex situations, provide business insights, and apply data strategy principles to solve moderately complex business problems.

Requirements

Master’s degree or the equivalent in Business, Engineering, Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 2 years of experience in data analysis, data science, statistics, or related experience; OR Bachelor’s degree or the equivalent in Business, Engineering, Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 4 years of experience in data analysis, data science, statistics, or related experience; OR 6 years of experience in data analysis, data science, statistics, or related experience.
SQL Expertise - Complex SQL (Big Query, Azure, MySQL, etc.), database optimization, nested queries, window functions, multi-terabyte datasets, and performance tuning.
Advanced ETL Development – Designing custom data transformation pipelines handling petabyte-scale Retail/Ecommerce datasets using Python, R, Spark, Apache Airflow & SQL.
Implementing proprietary data quality validation protocols.
Data Visualization - Tableau, Power BI, Looker, dashboard design, custom visualizations, and interactive reporting.
Advanced Statistical Modeling - Applying Bayesian & Frequentist time series forecasting techniques to Retail demand patterns, combined with experimental design methods for significance and hypothesis testing.
Machine Learning - ML solutions using scikit-learn or PyTorch, ensemble models, and neural networks.
Translating large volumes of data into actionable insights by performing descriptive, inferential, predictive statistics. t-test, Z-test, chi-square test, anova test, binomial test, one sample median test.
Business Translation - Aligning analytical initiatives with business goals, explaining technical concepts, and delivering data-driven solutions.
Retail/Ecommerce Analytics - Retail including omnichannel, forecasting, inventory optimization, and customer journey analytics.
Inventory Management Analytics - Developing inventory optimization models, predictive models, inventory analysis, and seasonal planning.

Responsibilities

Evaluate proposed business cases for projects and initiatives.
Translate business requirements into strategies, initiatives, and projects and aligns them to business strategy and objectives and drives the execution of deliverables.
Build and articulate the business case and return on investment and delivers work that has demonstrable value.
Challenge business assumptions on topics related to one's domain expertise.
Understand the priority order of requirements and service level agreements.
Define and identify the most suitable sources for required data that is fit for purpose, referring to external sources as needed.
Perform initial data quality checks on the extracted data.
Identify and recommend the most suitable visualization tools based on context.
Generate appropriate graphical representations of data and model outcomes.
Understand customer requirements to design appropriate data representation for complex data sets and drive User Experience designers and User Interface engineers to build front end applications.
Define application design based on customer requirements.
Build compelling stories based on context to integrate multiple pieces of information into cohesive insights.
Present to and influence diverse audiences using the appropriate data visualization frameworks and conveys clear messages through deep business and stakeholder understanding.
Customize communication style based on stakeholders and leverages relationships to drive behavioral change.
Ensure business needs are being met by evaluating the ongoing effectiveness of current plans, programs, and initiatives; consulting with business partners, managers, co-workers, or other key stakeholders; soliciting, evaluating, and applying suggestions for improving efficiency and cost-effectiveness; and participating in and supporting community outreach events.
Develop and evangelize data quality awareness.
Determine data quality strategy, metrics, and business rules for a business domain to ensure that data is fit for purpose.
Set and evaluate business domain data quality service level strategy.
Design and implement operational Data Quality Management (DQM) procedures.
Monitor operational DQM procedures and performance.
Lead data quality initiatives to deliver increased business performance and effectiveness.
Determine user accessibility and removes or restricts user access as needed.
Provide recommendations to leadership on needed updates or inputs into data governance policies, practices, and guidelines.
Promote the value of Knowledge Discovery in Data (KDD) for business managers.
Identify and applies suitable KDD tool basis business requirement.
Stay abreast of best practices in KDD techniques.
Test and evaluate multiple solutions, methods, and models to determine accuracy, validity, and applicability.
Establish standards for application and interpretation of statistical data.
Conduct statistical experiments (for example hypothesis tests, confidence intervals) and builds statistical models using packages like Statistical Analysis Systems (SAS).
Design, write, and publish numerous research documents on diverse topics.
Consult on complex situations to test hypotheses, anticipate pitfalls, prevent false conclusions, and advise others on avoiding them.
Provide insights to the business based on research findings.
Understand, articulate, interpret, and apply the principles of the defined strategy to unique, moderately complex business problems that may span one or main functions or domains.