Data Analyst

Key Responsibilities:

• Data Collection & Cleaning:
• Gather, clean, and validate data from various sources (internal and external systems, databases, APIs, etc.).
• Prepare raw data for analysis by transforming it into a structured format.
• Data Analysis:
• Analyze datasets to identify trends, patterns, and insights that can influence business decisions.
• Perform statistical analysis and interpret results to support business needs.
• Utilize tools like Excel, SQL, Python, R, or other analytics tools to analyze data.
• Reporting & Visualization:
• Create comprehensive reports, dashboards, and visualizations (using tools like Power BI, Tableau, or similar) to communicate findings effectively.
• Present data-driven insights to stakeholders in a clear and concise manner.
• Data Modeling & Querying:
• Develop and maintain complex queries (SQL, NoSQL) for data extraction, manipulation, and analysis.
• Create and update data models that represent business processes and workflows.
• Collaboration:
• Work closely with business stakeholders, data scientists, and other teams to understand business requirements and deliver actionable insights.
• Act as a liaison between technical teams and business units to identify and resolve data-related challenges.
• Data Quality & Governance:
• Ensure data accuracy, consistency, and completeness throughout the data pipeline.
• Enforce data governance and data management practices to ensure quality standards are met.
• Forecasting & Predictive Analytics:
• Develop forecasting models and assist in predictive analytics to aid in future planning and decision-making.
• Assist in the development and implementation of machine learning models (if applicable).
• Documentation & Reporting:
• Maintain thorough documentation on data analysis methodologies, data sources, and reporting techniques.
• Provide regular updates and insights to management and business leaders.

Required Skills and Qualifications:

• Technical Expertise:
• Proficiency in data analysis tools such as SQL, Excel, Python, R, or similar.
• Experience with data visualization tools like Power BI, Tableau, or others.
• Strong understanding of statistical analysis and methods.
• Database Knowledge:
• Familiarity with relational databases and data warehouse concepts.
• Experience with ETL processes and integration of data from multiple sources.
• Problem-Solving Skills:
• Strong analytical and problem-solving skills with the ability to think critically and logically.
• Ability to identify data anomalies, trends, and correlations.
• Communication Skills:
• Strong verbal and written communication skills to present findings to both technical and non-technical stakeholders.
• Ability to convey complex analytical findings in an easy-to-understand format.
• Attention to Detail:
• High attention to detail with a focus on accuracy and precision in data reporting and analysis.

Good to Have:

• Familiarity with machine learning or predictive modeling techniques.
• Experience with cloud-based data platforms (e.g., AWS, Google BigQuery, Azure).
• Knowledge of business intelligence tools and their use in generating insights from large datasets.
• Experience in data governance practices.