Tableau Analytics Engineer

* Is Tableau required: Yes.

* Is SQL required: Yes

* Strong analytic skills viewing bulk sets of data.

* Soft Skills right candidates: Open & effective communication, innovative, problem solving, relationship building using various internal platforms. Verbal presentation skills & willingness to accept positive criticism.

* 40% stakeholder management

*60% data analytics management

The Sr. Analyst Merchandising Analytics will support the efforts to provide subject matter expertise, project management, and analytical support to the development and execution of merchandising strategies. The Sr. Analyst will perform analysis of data, build presentations, and will solve business questions using their insights, analytics, and judgement. The Sr. Analyst will work with cross functional partners, including marketing, ops, strategy and others to ensure end to end thinking on strategy and execution. The Sr. Analyst will also support merchants directly by helping them understand their business, test pricing strategies, and helping them achieve their objectives.

Key Responsibilities:

• 50% Design, implement and measure pricing actions to understand the trade-off between units, sales and gross margin. Utilize learnings to help merchants reach financial objectives.
• 30% Conduct research to become subject matter expert on a specific segment of pricing and statistical analysis 10% Work across the organization and leverage cross functional teams to provide expert analytical support (i.e. domestic logistics or global supply chain costs 10% Develop and deliver influential presentations that summarize complex analyses and drive effective business decisions

Key Skills:

• Advanced SQL specifically Google BigQuery and an understanding of relational databases Tableau loading and manipulating large datasets into Tableau to build insights Excel knowledge of pivot tables, VLOOKUPS, graphing quick visualizations, building charts/graphs to understand statistical concepts.
• Data movement how to move large datasets Stakeholder communication easily summarizing complex information, especially in PowerPoint decks AI understanding of AI on how to do research on complex problems

Roles & Responsibilities Clarification THD CM Responses 1. Can you describe the day-to-day responsibilities of this role beyond what's listed? "-Develop new metrics for scalable reporting.

-Support ad-hoc data requests from leadership"

1. What percentage of time is expected to be spent on hands-on analytics vs. stakeholder communication? 60% Analytics; 40% Stakeholder communication 3. Will this role be more focused on pricing strategy or broader merchandising analytics? Merchanidising Analytics 4. Are there specific merchandising categories or business units this analyst will support? This role will support analytics for product teams as well as ad-hoc leadership reporting.
2. What types of business questions will the analyst be expected to solve regularly? Ad-hoc reporting + strategic metric development; understanding where to search or who to talk to to find where the data lives will be key 6. Is there a team of analysts, or will this person be working independently? Team of analysts

Technical Skills & Tools

1. How advanced should the candidate be in SQL and BigQuery? Are they expected to write complex queries or optimize performance? "-Advanced SQL. This role will work with complex Joins, Window Functions, and CTEs for data that is pulled in at the SKU, Store, Day grain.

-Understanding of how to trace back data discrepancies -Working with large datasets (>20M rows) and what that means to share data across BigQuery to stakeholders for analysis.

-Optimized queries for performance, e.g., how to process 20M+ rows of data daily -How to search the internet or internal resources to problem solve. "

1. What level of Tableau experience is required dashboard creation, data blending, or advanced calculations? "-Knowledge of how backend data populates a dashboard -Knowledge of impact of how a schema change can impact downstream users for a dashboard failing -How to efficiently filter large datasets in Tableau so the user experience isn't sacrificed -Understanding how to build a scalable dashboard, i.e., keeping complex calculated fields in BigQuery vs having these in Tableau. "
2. Are there specific data movement tools or platforms the candidate should be familiar with (e.g., ETL tools like Airflow, Fivetran)? "-Familiarity with data movement is a strong plus. THD uses internal data movement tools to manage data pipelines plus other tools like Google's Dataform and Microsoft's Power Apps.

-Familiarity of Jira and Confluence for tracking data updates.

-Familiarity of GitHub "

1. Is experience with AI tools or techniques required, or is it more about conceptual understanding? -The team is expected to be actively using AI to efficiently problem solve. AI is very good at code.
2. Will the analyst be expected to automate reports or build data pipelines? Yes

Education, Degrees & Certifications

1. Is a bachelor s degree required? If so, are there preferred fields (e.g., Statistics, Economics, Data Science)? Yes; Statistics, Economics, Data Science, Engineering, Business or related field 2. Is a master s degree or MBA preferred or required? Not required. MS in Data Science is a plus 3. Are there any certifications that would be considered a strong asset (e.g., Google Data Analytics, Tableau Desktop Specialist, SQL certifications)? Certifications are nice, but a portfolio demonstrating knowledge of the tool is better. - Please upload portfolio if candidate can share during submission.

4. Is formal training in statistical modeling or econometrics expected? Yes. A basic knowledge of statistics is expected