Senior Data Engineer - Azure Databricks & Spark

Expleo; Manila; On-site; PHP 892,800 - 1,339,200; Full time

Job summary

Expleo is looking for a Mid-level to Senior Data Engineer to build and support large-scale data pipelines using Azure Databricks, Spark, and Python/PySpark. The ideal candidate has 5+ years of hands-on experience and strong technical documentation skills.

The role demands strong communication abilities and familiarity with data lake architecture and ETL processes, as well as experience optimizing performance for TB-scale data processing. Collaboration with cross-functional teams is crucial.

Qualifications

• 5+ years of hands-on experience with Azure Databricks and Spark.
• Strong Python/PySpark and SQL coding skills.
• Experience optimizing performance for TB-scale data processing.
• Strong understanding of data lake architecture and ETL processes.
• Experience with Azure data analytics services.
• Data quality management experience.
• Strong technical documentation skills.
• Ability to clearly communicate technical concepts.
• Git experience.

Responsibilities

• Build and implement data pipelines using Azure Databricks and Spark.
• Write optimized Python/PySpark and SQL code for very large data processing.
• Design and maintain data lake architecture and ETL workflows.
• Manage data quality, integrity, and validation.
• Use Azure data services for storage, processing, and analytics.
• Create technical documentation, requirements, and testing documents.
• Collaborate with business and technical teams to understand requirements.

Skills

Azure Databricks

Spark

Python/PySpark

SQL

Data lake architecture

ETL processes

Data quality management

Technical documentation

Git

Job description

Expleo is looking for a Mid-level to Senior Data Engineer to build and support large-scale data pipelines using Azure Databricks, Spark, and Python/PySpark. The ideal candidate has 5+ years of hands-on experience and strong technical documentation skills.

The role demands strong communication abilities and familiarity with data lake architecture and ETL processes, as well as experience optimizing performance for TB-scale data processing. Collaboration with cross-functional teams is crucial.