Data Engineer

Key Responsibilities

• Retail Data Domain & System Exploration
• Explore, profile, and understand data structures across various retail source systems (ERP, CRM, Supply Chain, E-Commerce, Inventory Management, etc.).
• Data Ingestion & Pipeline Development (ETL/ELT)
• Build and maintain high-performance pipelines using SSIS and n8n to extract data from diverse source systems into the Data Warehouse.
• Advanced SQL & Query Optimization
• Write, tune, and optimize highly complex SQL queries, stored procedures, and window functions to handle large-scale data transformation and complex business logic efficiently.
• Data Warehouse Management
• Maintain and manage enterprise Data Warehouse architectures.
• Data Exploration & Cleansing
• Explore, profile, and clean complex datasets across legacy and modern business systems to ensure high data accuracy and readiness for analytical consumption.
• Semantic Layer & SSAS Management
• Build and optimize SSAS Tabular Models , managing data partitions, memory processing, and Row-Level Security (RLS).
• Data Technical Innovation
• Drive technical innovation through AI integration and advanced data capabilities.

Qualifications

• Experience: 2–3 years of hands-on experience in Data Engineering, ETL Development, or Data Warehousing.
• Domain Knowledge: Strong familiarity with retail business domains (e.g., POS data, omnichannel sales, inventory movement, store operations, product hierarchies, promotions).
• Mandatory Core Skills:
• SQL: Expert-level querying, indexing, and performance tuning.
• Microsoft BI Stack: High proficiency in SSIS and SSAS (Tabular Models) .
• ETL & Automation: Experience with n8n or similar orchestration tools; comfortable writing Python for data manipulation and task automation.
• Architecture & AI: solid understanding of Data Warehouse design principles and AI knowledge.
• Security & Governance: Clear understanding of data governance and Row-Level Security (RLS).
• Execution & Soft Skills: Strong commitment to project timelines, delivering quality data solutions under pressure in fast-paced environments.