Azure Data Engineer(SDE)

Role Overview: SDE/DE

Key Responsibilities

• Design and implement scalable, metadata-driven frameworks for data ingestion, quality, and transformation across both batch and streaming datasets.
• Develop and optimize end-to-end data pipelines to process structured and unstructured data, enabling the creation of analytical data products.
• Build robust exception handling, logging, and monitoring mechanisms for better observability and operational support.
• Take ownership of complex modules and lead the development of critical data workflows and components.
• Provide guidance to data engineers and peers on best practices, conduct code reviews, and enforces standards.
• Collaborate with cross-functional teams—including business consultants, data architects, scientists, and application developers—to deliver impactful analytics solutions.
  

External Skills And Expertise

• 5–10 years of total IT experience, including at least 3 years in big data engineering on Microsoft Azure.
• Strong SQL expertise with experience in writing, optimizing, and troubleshooting complex queries on Azure SQL, Synapse, or similar cloud databases.
• Solid understanding of Spark architecture and core APIs - RDD, Dataframe, Dataset.
• Strong understanding of the Databricks ecosystem, including Notebooks, Workflows, Unity Catalog, SQL Warehouse, Serverless compute, latest databricks features.
• Proven expertise in designing and developing scalable, high-performance data pipelines and automated workflows in Azure Databricks leveraging PySpark, Spark SQL.
• Experience in designing, building and orchestrating complex, parameterized pipelines in Azure Data Factory.
• Familiarity with both batch and streaming data processing.
• Solid understanding of data-modelling techniques (dimensional, 3NF) and data warehousing concepts
• Experience delivering at least one end-to-end Data Lakehouse solution in Azure using the Medallion Architecture
• Knowledge of different file formats such as Delta Lake, Avro, Parquet, JSON, and CSV
• Advanced programming, unit-testing, and debugging skills in Python, PySpark, and SQL
• Knowledge of data security and lifecycle management policies across Azure environment.
• Familiarity with DevOps practices (CI/CD, Git, automated deployments).
• Collaborative mindset with enthusiasm for working with stakeholders across the organization and taking ownership of deliverables.
  

Good To Have

• Exposure to developing LLM/Generative AI-powered applications.
• Knowledge about NoSQL database.
• Experience in supporting BI and Data Science teams in consuming the data in a secure and governed manner.
• Relevant Certifications on Microsoft Azure or Databricks are valuable addition.