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Data Pipeline Architect

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Databricks Data Engineer This role involves designing, developing and delivering scalable data pipelines using Spark, PySpark, SQL and Delta Lake. Develop ETL/ELT pipelines in Databricks using PySpark, Spark SQL and Delta Lake. Ingest, clean and transform data from diverse sources such as APIs, SQL databases, cloud storage and SAP/legacy systems. Create reusable pipeline frameworks, data validation logic and performance-tuned transformations. Design curated datasets and deliver insights through Power BI dashboards. Implement best practices for lakehouse development, orchestration and version control. Troubleshoot pipeline performance issues and ensure data accuracy, reliability and quality. Required Skills and Qualifications: Strong hands-on experience with Databricks (Spark, PySpark, Delta Lake). Advanced SQL skills for large-scale data processing. Experience integrating data from multiple structured and unstructured sources. Solid understanding of distributed computing, performance tuning and debugging Spark jobs. Power BI (reports, models, DAX preferred). Experience with CI/CD pipelines in an Azure environment. Nice to Have: Experience with data quality frameworks, lakehouse monitoring or DQX. Knowledge of Airflow, ADF, IoT or other tools is a plus. Experience with SAP data is a plus.