Data Analyst

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.