Google Data Analyst

The Data Analyst will be responsible for translating raw data into actionable insights, providing data-driven recommendations to support business strategy and decision-making. This role involves collecting, cleaning, analyzing, and visualizing data to identify trends, patterns, and opportunities for process and performance improvement. • Collect clean and analyze operational data and generate dashboards and reports tracking KPIs (e.g. order ingestion volumes, error rates , SLA adherence) • Conduct trend analysis and performance monitoring • Provide recommendations for process improvements based on data insights • Collaborate with Operational Analyst and Operation Managers for data validation Key Responsibilities and Duties: Data Acquisition and Preparation: • Extract, Transform, and Load (ETL) data from various primary and secondary data sources (e.g., internal databases, cloud platforms, external APIs). • Clean and preprocess raw data to ensure accuracy, consistency, and completeness, including identifying and correcting errors, managing missing data, and transforming formats. • Design and maintain databases (e.g., data warehouse models) and data systems to optimize data access and quality. Data Analysis and Interpretation: • Apply statistical methods and data analysis techniques to perform exploratory and diagnostic analysis to uncover trends, anomalies, and relationships within complex datasets. • Conduct hypothesis testing and develop models (e.g., regression analysis) to support business questions and predict outcomes. • Define, track, and report on Key Performance Indicators (KPIs) and metrics across various business functions (e.g., Marketing, Sales, Operations). Reporting and Visualization: • Design, develop, and maintain interactive dashboards and reports using visualization tools to present data in a clear, digestible format for both technical and non-technical audiences Collaboration and Improvement: • Collaborate with cross-functional teams (e.g., Data Engineering, Product, IT) to understand data requirements and deliver targeted, timely insights. • Proactively identify and recommend process improvements, system modifications, and operational changes based on data analysis. Required Qualifications and Skills: Essential Technical Skills (Hard Skills): • SQL (Structured Query Language): Advanced proficiency in writing complex queries, stored procedures, and managing relational databases. • Statistical Programming: Expertise in at least one statistical programming language (Python or R) for complex data analysis, statistical modeling, and automation (e.g., using libraries like Pandas, NumPy, Scikit-learn). • Data Visualization Tools: High proficiency in industry-standard tools like Tableau, Power BI, or Google Looker Studio for creating interactive dashboards. • Spreadsheet Tools: Advanced knowledge of Microsoft Excel (Pivot Tables, VLOOKUPs, functions, modeling). • Statistical Analysis: Strong foundation in descriptive and inferential statistics. Essential Workplace Skills (Soft Skills): • Critical Thinking and Problem-Solving: The ability to approach a business problem logically, ask the right questions, and find meaningful patterns in data. • Communication and Presentation: Excellent verbal and written communication skills to clearly articulate complex technical information and its business implications. • Attention to Detail: Meticulous approach to data cleaning and quality assurance to ensure the highest level of data integrity and reporting accuracy. Business Acumen: Understanding of business processes and the industry to ensure data analysis is relevant and actionable.

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