Major Responsibilities:

• Design, build, and maintain reliable ETL/ELT data pipelines for batch and near-real-time processing from internal and external sources using tools such as Azure Data Factory or Databricks workflows.
• Implement data validation, testing, and reconciliation checks (including dbt tests where applicable)
• Monitor pipeline health, performance, and reliability.
• Identify issues and escalate or collaborate with senior engineers to resolve them.
• Write SQL and Python queries for data extraction and transformation.
• Support documentation of processes, standards, and improvements.
• Support solution design by preparing data samples, documentation, or prototype queries.

Key Working Relationships:

• Data Team
• Business/Departmental Priority Setters
• Enterprise Systems Owners

Position Reports to:

Omar Bouidel

Travel Requirements:

Support solution design by preparing data samples, documentation, or prototype queries.

Minimum Requirements:

Experience:

2–4 years of hands-on experience in data engineering, data processing, or software engineering.

Technical Skills:

• SQL (advanced): joins, window functions, CTEs, performance tuning
• Python: data processing, APIs, automation, PySpark basics
• Data modeling: star/snowflake schemas, fact & dimension tables
• ETL/ELT pipelines: building, monitoring, and optimizing pipelines
• dbt Core / dbt Cloud: developing, scheduling, and maintaining models

• Design, build, and maintain reliable ETL/ELT data pipelines for batch and near-real-time processing from internal and external sources using tools such as Azure Data Factory or Databricks workflows.
• Implement data validation, testing, and reconciliation checks (including dbt tests where applicable)
• Monitor pipeline health, performance, and reliability.
• Identify issues and escalate or collaborate with senior engineers to resolve them.
• Write SQL and Python queries for data extraction and transformation.
• Support documentation of processes, standards, and improvements.
• Support solution design by preparing data samples, documentation, or prototype queries.

• SQL (advanced): joins, window functions, CTEs, performance tuning
• Python: data processing, APIs, automation, PySpark basics
• Data modeling: star/snowflake schemas, fact & dimension tables
• ETL/ELT pipelines: building, monitoring, and optimizing pipelines
• dbt Core / dbt Cloud: developing, scheduling, and maintaining models

• BA/BSc/HND
• 2–4 years of hands-on experience in data engineering, data processing, or software engineering.