Data Analyst

Job Description

Business Data Analyst | Scale.jobs

The Tone:
This is a full-time Business Data Analyst role at Scale.jobs, primarily located in Dallas, TX (Hybrid), with additional location options in Chicago, IL, New York City, San Francisco Bay Area, Washington, DC, Boston, or fully Remote. Scale.jobs focuses on transforming intricate behavioral and transactional data into clear, actionable insights, enabling clients to make superior business decisions. This position is vital for delivering deep analytical value, moving beyond mere dashboard maintenance. Successful analysts will be proactive, developing robust analytical frameworks, asking incisive questions, and communicating their findings with conviction to drive tangible client impact.

The TL;DR
• Role: Early Career
• Location: Hybrid, Dallas, TX
• Mission: Transforms complex behavioral and transactional data into clear, actionable insights to help clients make better business decisions.
• Tech Stack: SQL, Tableau, Power BI, Python (pandas, matplotlib/seaborn), GA4, Amplitude, Mixpanel, dbt, Redshift

What You’ll Actually Do
• Data Extraction & Transformation: Write complex SQL queries against large behavioral and transactional datasets to expertly extract, clean, and reshape data for in-depth analysis and comprehensive reporting.
• Performance Dashboarding: Build and diligently maintain impactful dashboards using Tableau or Power BI, ensuring stakeholders receive clear, accurate visibility into crucial business performance.
• Experimentation & Business Impact: Design and rigorously analyze A/B tests with appropriate statistical rigor, communicating results specifically in terms of tangible business impact, not just p-values.
• Workflow Automation: Automate recurring reporting workflows efficiently with Python (pandas) and SQL, proactively eliminating manual steps that slow down the team and enhance productivity.
• Exploratory Insight Generation: Conduct thorough exploratory analysis to proactively surface critical trends, identify anomalies, and uncover valuable opportunities that might not have been on the original question list.

The Must-Haves
• Background: An early career professional demonstrating 0–4 years of practical data analysis experience. This includes valuable work from internships, academic projects, or freelance engagements where real data challenges were addressed.
• Experience: Proven proficiency in writing advanced SQL queries, encompassing joins, aggregations, window functions, and subqueries, without requiring guidance. Additionally, strong experience in building and utilizing calculated fields within dashboards using Tableau or Power BI is essential.
• Skills: A solid foundational understanding of statistics, covering hypothesis testing, confidence intervals, and the distinction between correlation and causation. Practical Python basics, including pandas for data manipulation and matplotlib/seaborn for ad hoc analysis, are also required, alongside clear, structured communication abilities to effectively explain complex findings.
• Bonus: Desirable qualifications include prior exposure to analytics platforms like GA4, Amplitude, or Mixpanel, experience with data transformation tools such as dbt, or working with data warehouses like Redshift. Direct-to-consumer or e-commerce domain experience is also a plus.