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 Requirements · 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 : o Pandas (must have) o NumPy (must have) · Hands-on experience with interactive visualization : o 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 Must Have · 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 Tech Stack (Core) · 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)