Azure Data Engineer with Databricks

• Hands-on experience with implementing solutions with data lakes and pipelines (Databricks, Azure Data Factory).
• Proven experience in designing and implementing solutions in Azure cloud (knowledge of AWS, GCP clouds will be an additional advantage).
• Proficiency in SQL, PySpark, data modeling (3NF, star/snowflake schemas) and ELT/ETL design patterns (knowledge of popular ETL tools, including Azure Data Factory).
• Experience with Python or similar scripting languages for data transformation, orchestration, and automation.
• Familiarity with advanced Databricks features (Unity Catalog, MLflow, Jobs & Pipelines).
• Understanding cloud data architecture principles, including data Lakehouse concepts, Medallion architecture, API integration, and event-driven architecture.
• Experience with CI/CD and DevOps practices for data workflows (Git, Azure DevOps) and analytics integration (e.g., Power BI).
• Excellent communication and client-facing skills, with the ability to explain technical concepts clearly to team members and stakeholders.
• Strong collaboration skills across multidisciplinary teams.

,[Develop and implement scalable data processing solutions on Azure, transforming and cleaning large structured and unstructured datasets efficiently., Design, build, and maintain robust data pipelines integrating data from diverse sources (databases, APIs, streaming platforms)., Collaborate with cross-functional teams including data engineers, data scientists, and cloud architects to deliver end-to-end data platform solutions., Engage at all critical stages of project development with business analysts, source system experts, data architects, engineers, and product owners., Clearly communicate and document technical work, factoring in performance and cost considerations in all design decisions.] Requirements: SQL, PySpark, Scripting language, Data modeling, Snowflake, Data Lake, Databricks, Azure Data Factory, Azure Cloud, AWS, ETL, UNITY, Power BI, Python, MLflow, Cloud, API, Azure DevOps, CI/CD, Design Patterns, DevOps, GCP