Key Responsibilities:

• Design, build, and manage Azure Data Factory pipelines for data ingestion.

• Handle both batch and near real-time data ingestion using Azure Service Bus.

• Integrate structured, semi-structured, and unstructured data sources.

• Utilize formats such as Parquet, Avro, JSON, etc., and manage loading into the Bronze layer.

• Build and manage data engineering pipelines using Databricks (PySpark, SparkSQL).

• Implement Spark-based data transformation jobs from Bronze to Silver and Gold layers.

• Establish and manage Data Quality Framework using Databricks and Data Factory.

• Set up data security using RBAC and ACLs to manage permissions on sensitive datasets.

• Implement automation frameworks for pipeline execution using Databricks Workflows.

• Monitor pipelines using Azure Monitor, set up alerts and ensure SLAs are met.

• Optimize storage in ADLS Gen2 and use Azure Key Vault to secure credentials.

• Optimize costs using Azure Cost Management.

Technical Skills:

• Azure Data Factory for ETL/ELT operations.

• Databricks (PySpark, SparkSQL) for data transformations.

• Azure Data Lake Storage (ADLS Gen2).

• SQL Server, PostgreSQL, Cosmos DB, CRM, and ERP systems.

• Data Governance, RBAC, and ACLs for managing permissions.

• CI/CD tools: Azure DevOps, GitHub Actions, Terraform.

• Azure Monitoring, Alerting, and Logging tools.

PPL does not discriminate based on race, color, religion, or belief, national, social, or ethnic origin, sex, gender identity and/or expression, age, physical, mental, or sensory disability, sexual orientation, marital, civil union, or domestic partnership status, protected veteran status, citizenship status, family medical history or genetic information, family or parental status, or any other status protected under federal, state, or local law.