Azure Data Engineer

About Client

Qualification :

Relevant Experience :

MAIN SKILL

• 5+ years of experience in data modeling, with a strong focus on cloud-based environments (preferably Azure).
• Strong proficiency

  in SQL and experience working with Spark, Python, and other data processing languages.

• Proven experience with

  Azure Databricks, Azure Data Lake, and other Azure data services

• Strong experience in migrating data from Salesforce, Oracle, and SQL Server-based applications into cloud environments, specifically Azure.
• Expertise in designing and optimizing relational and dimensional data models (star/snowflake schemas, normalization/denormalization, etc.).
• In-depth understanding of ETL processes, data integration, and pipeline optimization.
• Familiarity with data governance, security policies, and compliance standards in cloud environments.
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams.
• Ability to translate business requirements into scalable and optimized data models.
• Strong problem-solving and analytical skills to troubleshoot and optimize complex data modeling issues.

Key Responsibilities:

• Design, implement, and optimize data models in Azure Databricks to support large-scale data migration projects from Salesforce, Oracle, and SQL Server-based applications.
• Collaborate with business stakeholders, data architects, and data engineers to understand data requirements and translate them into scalable, high-performance data models.
• Develop conceptual, logical, and physical data models to ensure data consistency, integrity, and optimal performance in the Azure cloud environment.
• Work closely with the data engineering team to ensure smooth integration of data from

  Salesforce, Oracle, and SQL Server into Azure Databricks using ETL pipelines.

• Optimize and fine-tune data models for large datasets, focusing on performance, scalability, and cost efficiency within the

  Azure platform.

• Ensure data governance, security, and compliance standards are met when designing and implementing data models and pipelines.
• Develop and maintain documentation for data models, including relationships, entities, transformations, and business rules.
• Implement best practices for data modeling, including naming conventions, data types, and normalization/denormalization strategies.
• Collaborate with data scientists and analysts to ensure the data models support business intelligence, reporting, and machine learning initiatives.
• Conduct data quality assessments and validate the accuracy of migrated data.

Troubleshoot, resolve, and optimize data model-related performance and data quality issues across the migration process.

Location :

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