Data Analyst

Roles and responsibilities

• Bachelor’s degree in computer science, Information Technology, or a related field.
• 1-2 years of experience working with Tableau, Power BI, or other business intelligence tools.
• Proficiency in SQL scripting and knowledge of ETL processes.
• Familiarity with databases such as MySQL, PostgreSQL, or MongoDB.
• Experience with cloud platforms like AWS, Google Cloud, or Azure.
• Experience in scheduling scripts and automating stored procedures.
• Ability to manage large datasets and ensure data quality.
• Knowledge of Python or R for data analysis is a plus.
• Strong data analytical and problem-solving skills.
• Excellent communication skills and the ability to work in a team environment.
  

Key Responsibilities

• Design and develop interactive dashboards using Tableau and Power BI to visualize key business metrics.
• Write and optimize SQL scripts for data extraction, transformation, and loading (ETL) functions across different databases.
• Schedule and automate SQL scripts and stored procedures to ensure smooth data operations.
• Collaborate with cross-functional teams to gather requirements and provide data insights for decision-making.
• Maintain and manage databases such as MySQL, PostgreSQL, or MongoDB to ensure data accuracy and availability.
• Work with cloud services (e.g., AWS, Google Cloud, Azure) to store, process, and manage data.
• Analyze large datasets to identify trends, patterns, and actionable insights.
• Assist in improving data quality and reporting processes.

• Technical Skills

• Data Management Tools: Proficiency in tools like SQL, Excel, or Python for data manipulation and analysis.
• Data Visualization: Creating visual representations of data using software like Tableau, Power BI, or similar tools.

• Problem Solving

• Critical Thinking: Approaching problems logically and developing data-driven solutions.
• Hypothesis Testing: Formulating and testing hypotheses based on data analysis.

• Communication Skills

• Reporting Findings: Presenting analysis results to stakeholders in a clear and concise manner.
• Collaboration: Working with cross-functional teams to understand data needs and provide insights.

• Attention to Detail

• Data Quality Assurance: Ensuring data accuracy and consistency through rigorous validation and cleaning processes.
• Thoroughness: Carefully examining data to identify anomalies or discrepancies.

Desired candidate profile

Analytical Skills

1. Data Interpretation: Analyzing complex data sets to identify trends, patterns, and insights.
2. Statistical Analysis: Applying statistical techniques to evaluate data and inform decision-making.

Technical Skills

1. Data Management Tools: Proficiency in tools like SQL, Excel, or Python for data manipulation and analysis.
2. Data Visualization: Creating visual representations of data using software like Tableau, Power BI, or similar tools.

Problem Solving

1. Critical Thinking: Approaching problems logically and developing data-driven solutions.
2. Hypothesis Testing: Formulating and testing hypotheses based on data analysis.

Communication Skills

1. Reporting Findings: Presenting analysis results to stakeholders in a clear and concise manner.
2. Collaboration: Working with cross-functional teams to understand data needs and provide insights.

Attention to Detail

1. Data Quality Assurance: Ensuring data accuracy and consistency through rigorous validation and cleaning processes.
2. Thoroughness: Carefully examining data to identify anomalies or discrepancies.

Knowledge of Business Operations

1. Understanding of Industry: Familiarity with the specific industry or sector to contextualize data analysis.
2. Key Performance Indicators (KPIs): Identifying and tracking relevant metrics that impact business performance.

Project Management

1. Task Prioritization: Managing multiple projects and deadlines effectively.
2. Goal Setting: Defining clear objectives for analysis projects and measuring progress.

Continuous Learning

1. Staying Updated: Keeping up with the latest tools, technologies, and best practices in data analysis.
2. Skill Development: Pursuing additional training or certifications in data analytics or related fields.