Data Analyst & Visualization

JD:

• Discovery & Requirements Gathering (10% of Time)

o Collaborate with business stakeholders (e.g., department heads, managers) to understand their key challenges and informational needs

o Translate vague business questions ("why are sales down?") into specific, data-driven hypotheses and analysis plans.

o Define and agree upon Key Performance Indicators (KPIs) and success metrics for projects.

• Data Acquisition & Preparation (Data Wrangling) (40% of Time)

o Extract data from various sources (SQL databases, CRM platforms like Salesforce, marketing platforms like Google Analytics, ERP systems, flat files like CSVs).

o Clean and transform data to ensure accuracy and usability: handle missing values, remove duplicates, standardize formats, and create new calculated fields.

o Model and structure data for analysis, often creating a single source of truth or a "dataset" optimized for reporting in a BI tool.

o Document data sources, assumptions, and transformation logic.

• Analysis & Insight Generation (25% of Time)

o Perform Exploratory Data Analysis (EDA) to identify trends, patterns, correlations, and anomalies in the data.

o Conduct statistical analysis to test hypotheses and validate the significance of findings.

o Synthesize analysis into core insights and actionable recommendations.

o Create summary reports and presentations to communicate findings to stakeholders.

• Data Visualization & Dashboard Development (20% of Time)

o Design wireframes and mock-ups for dashboards and reports, focusing on user experience (UX) and intuitiveness.

o Develop interactive, visually appealing, and performance dashboards using BI tools (Tableau, Power BI, etc.).

o Apply principles of data visualization (appropriate chart types, use of color, minimizing clutter) to ensure clarity and effectiveness.

o Implement features like filters, drilldowns, and tooltips to enable self-service exploration for end users.

• Deployment & Maintenance (5% of Time)

o Publish dashboards to production environments (e.g., Tableau Server, Power BI Service) and manage access permissions.

o Train end-users on how to use dashboards and interpret the data.

o Monitor and troubleshoot dashboard performance and data refresh schedules.

o Iterate existing reports based on user feedback and changing business needs.

Qualifications:

• Bachelor’s degree in computer science, Information Systems, Statistics, Mathematics, or related field.

• 3+ years of professional experience in a data analysis role with a proven portfolio of data visualization projects.

• Technical Skills (Hard Skills):

o Data Querying & Manipulation: Proficiency in SQL is essential for extracting, aggregating, and transforming data from relational databases

o Data Analysis & Statistical Literacy: Proficiency in a data analysis language or environment, preferably Python or R, understanding of fundamental statistical concepts (descriptive statistics, correlation, regression, hypothesis testing) to validate findings.

o Data Visualization & BI Tools: Expertise in at least one major business intelligence (BI) and data visualization tool, such as: Tableau, Power BI, Looker Studio etc. and ability to create interactive dashboards, calculated fields, and manage data models within these tools.

o Spreadsheet Proficiency: Advanced skills in Microsoft Excel or Google Sheets (pivot tables, complex formulas, data cleaning).

o Data Modelling & ETL Concepts: Understanding of data warehousing concepts (star/snowflake schema) & Familiarity with ETL (Extract, Transform, Load) processes and tools

• Non-Technical Skills (Soft Skill)

o Storytelling & Communication: Ability to translate complex technical findings into clear, concise, and actionable business recommendations for non-technical stakeholders.

o Critical Thinking & Problem-Solving: A curious and analytical mindset to ask the right questions, identify root causes, and solve business problems with data.

o Attention to Detail: Meticulousness in data validation and analysis to ensure accuracy and integrity of insights.

o Business Acumen: Strong understanding of the industry and key business metrics (KPIs) to ensure analysis is relevant and valuable.

o Collaboration & Stakeholder Management: Ability to work effectively with cross-functional teams (e.g., marketing, sales, finance, engineering) to gather requirements and present results