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: Solid 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.

📌 Data Analyst (Bengaluru)
🏢 Kaar Technologies
📍 Bengaluru