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.