Data Analyst

Job Title- Data Analyst

Experience- 6 plus years.

Location- Viman Nagar Pune.

Timings- 11 am- 8 pm.

Be part of AcquireX team that unleashes the power of leading-edge technologies to help improve e-commerce processes in the e-commerce world. We are looking for a Data Analyst to analyze, transform, and validate data that powers analytics, reporting, and operational decision-making. You will work with multiple data sources and internal systems to ensure data accuracy, consistency, and usability.

Key Responsibilities

• Transform, normalize, and analyze data for business insights and reporting
• Write and optimize SQL queries across analytical databases
• Work with PostgreSQL, MS SQL Server, Redis/Oracle (ClickHouse is a plus)
• Build and maintain analytical datasets using Python and SQL
• Ensure high data quality by identifying duplicates, errors, and inconsistencies
• Create visualizations and dashboards for stakeholders
• Document datasets, metrics, and analysis logic
• Collaborate with engineers, analysts, and business teams
  
  
  

• 6+ years of experience as a Data Analyst (or Analytics-focused role)
• EDA (Exploratory Data Analysis)
• Strong SQL skills (joins, aggregations, window functions, optimization)
• Proficiency in Python for data analysis:
• Pandas (must have)
• NumPy (must have)
• Hands-on experience with interactive visualization:
• Plotly (must have)
• Experience working with CSV, JSON, and XML data formats
• Familiarity with GCP and working knowledge of Azure
• Understanding of data normalization and multi-source analysis
• English proficiency: Intermediate
  
  
  

• Experience with ClickHouse is a plus
• Familiarity with Dask or Polars for large datasets
• Experience with Matplotlib or Seaborn
• Exposure to scikit-learn, SciPy, or Statsmodels
• Experience with e-commerce analytics or marketplaces
  
  
  

• Languages: SQL, Python
• Analysis: Pandas, NumPy
• Visualization: Plotly (primary), Matplotlib / Seaborn (plus)
• Big Data: Dask, Polars
• Databases: PostgreSQL, MS SQL Server, Redis/Oracle, ClickHouse (plus)
• Environment: Jupyter Notebook / JupyterLab, Dash
• Cloud: GCP, Azure (basic)