Data Platform Engineer

Act as a Senior Analyst and subject matter contributor for Azure-based analytics solutions.
Analyze business and data requirements and translate them into technical designs aligned with Azure analytics best practices.
Design and develop data ingestion pipelines using Azure Data Factory and supported connectors.
Build and manage data storage and analytics layers using Azure Data Lake Storage and Azure Synapse Analytics.
Develop and optimize data transformations using SQL, PySpark, and Spark-based processing.
Implement structured data modeling and layering standards for curated and consumption-ready datasets.
Support Power BI dataset development including measures, relationships, refresh strategies, and basic performance tuning.
Perform data validation and reconciliation to ensure data accuracy, completeness, and consistency.
Collaborate with architects and leads on design decisions and solution improvements.
Troubleshoot and resolve data pipeline failures, data issues, and performance bottlenecks.
Participate in code reviews, documentation, and knowledge-sharing sessions.
Support production deployments, enhancements, and ongoing operational activities.
Professional & Technical Skills: - Must-Have Skills with Microsoft Azure Analytics as listed below
Hands-on experience with Microsoft Azure Analytics services, including:
o Azure Data Factory for data integration and orchestration
o Azure Synapse Analytics (SQL Pools)
o Azure Data Lake Storage Gen2
Working knowledge of PySpark or Spark SQL for data processing.
Understanding of data warehousing concepts (fact/dimension tables, SCDs).
Experience with Power BI datasets and semantic models.
Familiarity with data ingestion patterns (full loads, incremental loads).
Ability to debug and resolve data pipeline and transformation issues.
Basic understanding of Git-based version control and deployment practices.
Strong proficiency in SQL for querying, transformations, and analytical data modeling.