Data Warehouse Engineer

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 skills.

Requirements:

• 24 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 willingness to learn and adapt.
• Ability to work effectively in a collaborative, fast-paced team environment.

Preferred Qualifications:

• Experience working with cloud platforms (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.