Senior Data Analyst

at  Everest Search Partners

SUMMARY: Everest client seeks a Senior Data Analyst to work on their analytics team. This is a critical role responsible for extracting valuable insights from sales and operations data, facilitating informed decision-making and to enhance overall company performance. The ideal candidate will have a strong background in data analysis in retail and/ or e-commerce sales, and the ability to extract actionable strategies from data.

QUALIFICATIONS

• Ability to work a HYBRID SCHEDULE: In office Monday-Thursday, remote on Friday.
• Detail-oriented with a passion for accuracy in data analysis
• Ambitious, innovative individual, not afraid of change and moving at a fast pace
• Comprehensive understanding of best practices in Systems Analysis, Business Analysis, and Business Intelligence
• Experience with large, complex data warehouse projects in retail environments, pulling data from multiple sources
• Understanding of data modeling; ability to explain and interpret conceptual, logical, and physical models
• Strong work ethic and excellent communication skills
• Ability to work in an environment with multiple technology infrastructures
• Must possess a continuous improvement mentality and is always looking for opportunities to drive improved efficiency and effectiveness in our operations
• Ability to work independently while building strong relationships and maintaining rapport with business colleagues
• Comfort with change and the ability to adapt easily to new systems
• Proactive and self-motivated, with the ability to prioritize and manage multiple tasks
• Personal qualities of integrity, credibility, and a commitment to the organization’s mission

REQUIRED EDUCATION AND EXPERIENCE

• Bachelor’s degree in Business, Statistics, Economics, Computer Science, or a quantitative field or Relevant Experience
• 2+ years of experience in an analytical or strategic role
• Experience with advanced ETL tools including Tableau Data Prep, PowerQuery, SQL Server Reporting Services and others
• Experience working in cross-functional teams focused on various analytics areas (Business/Financial Analysis, Promotional Analytics, Customer Analytics, Pricing, Merchandising, Media Analytics, etc.)
• Advanced knowledge and experience with big data ecosystems, relational databases, SQL, and business intelligence tools (Tableau or Power BI, etc.)
• Experience developing analytic techniques such as forecasting, predictive modeling, segmentation analysis, simulation modeling, and data mining
• Understanding and capabilities with Python, R scripting and other advanced data coding languages
• Advanced skill with Excel and Microsoft Office
• Netsuite experience preferred
• Consumer packaged goods industry experience; retail experience a plus

• Develop and enhance data collection processes, document data definitions and catalog metadata requirements for enterprise BI and analytical applications.
• Develop and maintain key performance indicators (KPIs) for sales and operational performance to support business initiatives, creating clear and compelling visualizations of data insights.
• Identify trends, patterns, and anomalies in sales data, providing actionable insights for business optimization.
• Apply advanced quantitative methods, data mining, modeling, and analytical tools to solve complex business problems.
• Extract, clean, and analyze large datasets related to retail and e-commerce sales.
• Gather end-user reporting and dashboard requirements.
• Provide training, resolve questions, assess user needs, and recommend changes to data systems.
• Apply typical data science techniques (classification, regression, optimization).
• Utilize data exploration and analytical tools, advising business stakeholders.
• Conduct research and analysis, applying strong critical and lateral thinking skills.
• Translate data into actionable recommendations, actively solving ambiguous, unstructured problems.
• Work within a cross-functional, team-driven structure to present and explain deliverables to non-technical stakeholders.
• Build strong business partnerships to enhance enterprise utilization of data.
• Share and implement best practices.
• Mine/validate/cleanse data and provide ad hoc analysis upon request.