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.