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.
