Data Analyst – Retail Industry

Job Description: Data Analyst – Retail Industry

Experience: 4+ Years

Location: India – Remote

Industry: Retail

Employment Type: Full-time

Job Summary

We are looking for an experienced Data Analyst with strong Python skills and a solid understanding of Data Lake architecture, data engineering, and ETL/ELT processes. The candidate will analyze large-scale retail datasets, support data pipeline development, and deliver actionable insights to improve business performance.

Key Responsibilities

Analyze large retail datasets related to sales, inventory, pricing, promotions, customer behavior, and product performance.

Work with Data Lakes and understand Data Lake architecture, including data ingestion, storage, processing, and consumption.

Develop, maintain, and optimize data processing workflows using Python and SQL.

Support the design, development, and maintenance of ETL/ELT pipelines for integrating data from multiple retail systems.

Ensure data quality, accuracy, consistency, and integrity across data platforms.

Perform ad hoc analysis and develop reports and dashboards to monitor retail KPIs.

Collaborate with business stakeholders and technical teams to gather requirements and translate them into data solutions.

Support data modeling, data validation, data reconciliation, and troubleshooting of pipeline issues.

Document analysis findings, data requirements, process changes, and their business impact.

Work with cloud-based data platforms and support scalable data engineering and analytics solutions.

Required Skills

Experience: 4+ years of experience in Data Analytics, Data Engineering, or a related field.

Python: Strong hands-on experience in Python for data processing, transformation, automation, and large-scale data analysis.

Data Lake Architecture: Strong understanding of Data Lakes, Data Lake architecture, data ingestion, storage layers, and data processing workflows.

Data Engineering: Hands-on experience with ETL/ELT processes, data pipelines, data integration, and data transformation.

SQL: Strong knowledge of SQL, including complex queries, joins, aggregations, and data validation.

Cloud Platforms: Experience with Azure or GCP data services.

Databricks: Understanding or experience with Databricks and distributed data processing.

Data Modeling: Knowledge of data modeling, data warehousing, and Lakehouse architecture.

Visualization: Experience with Power BI, Tableau, or Excel for reporting and dashboards.

Analytical Skills: Strong problem-solving, analytical, documentation, and stakeholder communication skills.

Good-to-Have Skills

Experience with Azure Data Lake Storage (ADLS), Azure Data Factory, or Google Cloud Storage.

Hands-on experience with Azure Databricks, PySpark, or Apache Spark.

Knowledge of Delta Lake, medallion architecture (Bronze, Silver, and Gold layers), and data lakehouse solutions.

Experience analyzing retail datasets from POS systems, e-commerce platforms, inventory management, ERP, or CRM systems.

Understanding of retail KPIs, demand forecasting, inventory optimization, and sales performance analysis.

Relevant certifications in Azure, Databricks, or data engineering.

Preferred Industry Experience

Experience in retail, e-commerce, supermarkets, fashion and apparel, or omnichannel retail.

Familiarity with retail sales transactions, product master data, customer data, pricing, promotions, and inventory analytics.

Education

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.