emedic store is an online specialised marketplace for pharmaceutical and medical products. We harness the power of technology to deliver innovative, convenient, and affordable online goods and services to consumers.
Job Overview:
- The Senior Data Analyst is responsible for hands-on gathering, processing, and analyzing data to support strategic business decisions. This role involves working with various data sources and tools to ensure clean, accurate data and providing insights through detailed reports and visualizations. The Data Analyst collaborates closely with departments like product, engineering, marketing, sales and operations; aligning their work with the organization's objectives by developing and tracking KPIs. This role is integral to optimizing data workflows, ensuring data quality, and communicating actionable insights to stakeholders.
Responsibilities and Duties
- Core Responsibilities - Collects, processes, and analyzes data to generate insights; creates reports and visualizations; supports business decisions with data-driven recommendations; may work closely with business intelligence and analytics teams.
- Data Collection - Extracts data from various sources, including databases, APIs, spreadsheets, and data warehouses; may set up automated data pipelines and ensure data is clean and complete for analysis.
- Data Cleaning & Preparation - Identifies and corrects data inconsistencies, fills in missing values, and removes duplicate records; prepares datasets for analysis, which may involve transforming variables, handling outliers, and standardizing data formats.
- Data Modeling - Designs and structures data frameworks to represent real-world scenarios accurately, defining relationships between data points; ensures data consistency, integrity, and scalability across systems, creating a solid foundation for data analysis and decision-making. Focuses on optimizing data architecture to support complex analyses and business requirements.
- Data Analysis Techniques - Applies statistical methods, including descriptive statistics, trend analysis, and hypothesis testing; may use advanced techniques like regression analysis, clustering, and machine learning models, depending on business needs.
- Database Management - Works with relational databases (e.g., SQL Server, MySQL) and NoSQL databases to extract and query data; may optimize queries to ensure efficient data retrieval and processing.
- Data Visualization - Transforms analysis results into visual formats (e.g., charts, graphs, dashboards) to effectively communicate insights; focuses on making data accessible and actionable for non-technical stakeholders.
- Reporting - Creates regular and ad-hoc reports for different departments; ensures reports are accurate, relevant, and align with business objectives; may develop automated reporting processes for efficiency.
- Data Governance - Follows data governance policies, including compliance with privacy and security regulations (e.g., HIPAA, GDPR); ensures data use aligns with organizational standards and ethical guidelines.
- Automation - Builds automated workflows and scripts to streamline data extraction, transformation, and analysis; may set up ETL processes to reduce manual work and improve consistency in data handling.
- End-User Engagement - Engages with report users to gather feedback on insights, usability, and relevance; iteratively refines data products based on feedback to enhance decision support across teams.
- Presentation Skills - Presents findings to stakeholders, often translating complex data insights into understandable terms; uses storytelling techniques to convey the impact of data on business strategies and outcomes.
- Collaboration with Stakeholders - Works closely with departments such as marketing, sales, finance, and operations to understand their data needs; gathers requirements and provides insights tailored to different functional areas.
- Business Acumen - Understands industry and company-specific factors that influence data analysis; tailors analyses to answer key business questions and support decision-making aligned with organizational goals.
- Data Quality Assurance - Ensures data integrity and reliability by conducting quality checks and verifying accuracy; documents data sources and maintains data governance practices to reduce errors and enhance trust in data.
- Hypothesis Testing - Conducts A/B testing and experiments to evaluate business strategies and product features; uses statistical techniques to validate findings and make recommendations.
- Predictive Analytics - May use machine learning models for predictive analytics, including forecasting trends, customer segmentation, and propensity modeling; uses algorithms to identify patterns that inform business strategies.
- KPI & Metrics Development - Works with leadership and departments to define key performance indicators (KPIs) and other metrics; monitors KPIs and provides insights to track progress toward business goals.
- Documentation - Documents data sources, methodologies, and analysis processes to ensure transparency and repeatability; maintains a data dictionary or similar resources for clarity on datasets and variables.
- Technical Problem-Solving - Troubleshoots data issues, optimizes queries, and addresses bottlenecks in data workflows; identifies solutions to technical challenges in analysis and reporting.
- Exploratory Data Analysis (EDA) - Conducts EDA to uncover trends, anomalies, and relationships within data; uses data visualizations and summary statistics to develop a foundational understanding before deeper analysis.
- Continuous Learning - Stays updated on the latest tools, techniques, and best practices in data analysis; seeks to continuously improve analytical skills, understanding of industry trends, and knowledge of emerging data technologies.
- Project Management - Manages analysis projects, ensuring timely completion and alignment with business needs; often handles multiple requests simultaneously and prioritizes based on business impact and urgency.
- Tool Proficiency - Proficient with tools such as SQL, Excel, Python, R, and statistical software (e.g., SAS, SPSS); uses data visualization software like Tableau, Power BI, or Looker to create meaningful visuals and reports.
Qualifications and Skills:
- Bachelor’s degree in Computer Science, Data Science, Statistics, Computer Science, or a related field
- Proficient in SQL and data visualization tools such as Tableau, Power BI, or Looker
- Excellent communication skills for presenting complex data insights in a clear and concise manner to non-technical and technical stakeholders
- Strong analytical skills with experience in statistical analysis, regression, and hypothesis testing
- Familiarity with programming languages such as Python or R for data analysis
- Experience working with relational databases (e.g., SQL Server, MySQL) and NoSQL databases
- Knowledge of data governance, quality assurance, and data privacy regulations (e.g., HIPAA, GDPR)
- Experience with database management systems
Method of Application
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