Description

Design and maintain data pipelines and lakehouse architectures within an Azure environment, ensuring data quality across global projects.

This role is on-site.

Responsibilities

• Build and manage data pipelines using Spark-SQL, PySpark, and Azure Data Factory.
• Maintain Lakehouse architecture utilizing ADLS and Databricks.
• Execute data preparation tasks including cleaning, normalization, deduplication, and type conversion.
• Collaborate with DevOps to deploy solutions and manage production environments.
• Identify, troubleshoot, and resolve data errors through corrective actions and root cause analysis.

Required Skills

• 5+ years of experience in data engineering, data warehousing, or data integration.
• Proficiency in Python, Java, or Scala.
• Strong expertise in SQL and database management systems.
• Hands-on experience with Azure, AWS, or GCP.
• Experience with big data technologies including Spark, Hadoop, or NoSQL databases.
• Ability to design and optimize data models for diverse data types.
• Proven experience building and maintaining ETL pipelines and data workflows.

Preferred Skills

• Knowledge of data governance, security, and compliance best practices.
• Experience leading and participating in global projects, documenting changes, and managing data migrations.

Key Skills

Education

Bachelor's degree in Computer Science