Data Analyst

Job Title- Data Analyst

Experience- 3 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

· 3+ 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)