Data Analyst L2

Job Summary

The Data Analyst L2 is responsible for collecting, processing, analyzing, and visualizing data to support business decision-making. This role requires strong analytical skills, a solid understanding of data modeling, and the ability to communicate insights effectively to both technical and non-technical stakeholders.

Key Responsibilities

• Analyze complex datasets to identify trends, patterns, and actionable insights.
  
• Design, build, and maintain dashboards and reports using tools like Power BI, Tableau, or Looker.
  
• Develop and optimize SQL queries for data extraction and transformation.
  
• Work closely with cross-functional teams (Engineering, Product, Finance, Operations) to define metrics and KPIs.
  
• Perform data cleaning, validation, and quality assurance to ensure accuracy and consistency.
  
• Automate recurring reports and analytics processes where possible.
  
• Collaborate with Data Engineers to enhance data pipelines and warehouse performance.
  
• Support predictive and prescriptive analytics initiatives using statistical or ML techniques (if applicable).
  
• Document data sources, business rules, and report logic for transparency and reproducibility.
  

Required Skills and Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Economics, or related field.
  
• 3–5 years of professional experience in data analysis or business intelligence.
  
• Strong SQL skills (joins, CTEs, window functions, optimization).
  
• Proficiency in data visualization tools such as Power BI, Tableau, or Looker.
  
• Experience working with large datasets and data warehouses (e.g., Snowflake, BigQuery, Redshift).
  
• Proficient in Excel and at least one scripting language (Python, R).
  
• Understanding of statistical methods and data modeling.
  
• Excellent analytical thinking, communication, and problem-solving skills.
  

Preferred Qualifications

• Experience with ETL tools (e.g., Alteryx, Airflow, dbt).
  
• Familiarity with cloud platforms (AWS, GCP, Azure).
  
• Exposure to version control (Git) and agile methodologies.
  
• Experience with A/B testing and experimentation.
  

Performance Indicators

• Accuracy and timeliness of data reporting.
  
• Business impact of insights provided.
  
• Level of automation achieved in analytics processes.
  
• Cross-functional collaboration effectiveness.