Data Warehouse Engineer

Pando is a global leader in supply chain technology, building the world's quickest time-to-value Fulfillment Cloud platform. Pando’s Fulfillment Cloud provides manufacturers, retailers, and 3PLs with a single pane of glass to streamline end-to-end purchase order fulfillment and customer order fulfillment to improve service levels, reduce carbon footprint, and bring down costs. As a partner of choice for Fortune 500 enterprises globally, with a presence across APAC, the Middle East, and the US, Pando is recognized as a Technology Pioneer by the World Economic Forum (WEF), and as one of the fastest growing technology companies by Deloitte.
Role Overview

As a Junior Data Warehouse Engineer at Pando, you’ll work within the Data & AI Services team to support the design, development, and maintenance of data pipelines and warehouse solutions. You'll collaborate with senior engineers and cross-functional teams to help deliver high-quality analytics and reporting solutions that power key business decisions. This is an excellent opportunity to grow your career by learning from experienced professionals and gaining hands-on experience with large-scale data systems and supply chain technologies.

Key Responsibilities

• Assist in building and maintaining scalable data pipelines using tools like PySpark and SQL-based ETL processes.
  
• Support the development and maintenance of data models for dashboards, analytics, and reporting.
  
• Help manage parquet-based data lakes and ensure data consistency and quality.
  
• Write optimized SQL queries for OLAP database systems and support data integration efforts.
  
• Collaborate with team members to understand business data requirements and translate them into technical implementations.
  
• Document workflows, data schemas, and data definitions for internal use.
  
• Participate in code reviews, team meetings, and training sessions to continuously improve your skills

Requirements

• 2–4 years of experience working with data engineering or ETL tools (e.g., PySpark, SQL, Airflow).
  
• Solid understanding of SQL and basic experience with OLAP or data warehouse systems.
  
• Exposure to data lakes, preferably using Parquet format.
  
• Understanding of basic data modeling principles (e.g., star/snowflake schema).
  
• Good problem-solving skills and a willingness to learn and adapt.
  
• Ability to work effectively in a collaborative, fast-paced team environment.

Preferred Qualifications

• Experience working with cloud platforms (e.g., AWS, Azure, or GCP).
  
• Exposure to low-code data tools or modular ETL frameworks.
  
• Interest or prior experience in the supply chain or logistics domain.
  
• Familiarity with dashboarding tools like Power BI, Looker, or Tableau.