Databricks Engineer

Data Pipeline Development:

Design and implement end-to-end data pipelines in Azure Databricks, handling ingestion from various data sources, performing complex transformations, and publishing data to Azure Data Lake or other storage services.
Write efficient and standardized Spark SQL and PySpark code for data transformations, ensuring data integrity and accuracy across the pipeline.
Automate pipeline orchestration using Databricks Workflows or integration with external tools (e.g., Apache Airflow, Azure Data Factory).
Data Ingestion & Transformation:

Build scalable data ingestion processes to handle structured, semi-structured, and unstructured data from various sources (APIs, databases, file systems).
Implement data transformation logic using Spark, ensuring data is cleaned, transformed, and enriched according to business requirements.
Leverage Databricks features such as Delta Lake to manage and track changes to data, enabling better versioning and performance for incremental data loads.
Data Publishing & Integration:

Publish clean, transformed data to Azure Data Lake or other cloud storage solutions for consumption by analytics and reporting tools.
Define and document best practices for managing and maintaining robust, scalable data pipelines.
Data Governance & Security:

Implement and maintain data governance policies using Unity Catalog, ensuring proper organization, access control, and metadata management across data assets.
Ensure data security best practices, such as encryption at rest and in transit, and role-based access control (RBAC) within Azure Databricks and Azure services.
Performance Tuning & Optimization:

Optimize Spark jobs for performance by tuning configurations, partitioning data, and caching intermediate results to minimize processing time and resource consumption.
Continuously monitor and improve pipeline performance, addressing bottlenecks and optimizing for cost efficiency in Azure.
Automation & Monitoring:

Automate data pipeline deployment and management using tools like Terraform, ensuring consistency across environments.
Set up monitoring and alerting mechanisms for pipelines using Databricks built-in features and Azure Monitor to detect and resolve issues proactively.
Requirements

Data Pipeline Expertise: Extensive experience in designing and implementing scalable ETL/ELT data pipelines in Azure Databricks, transforming raw data into usable datasets for analysis.
Azure Databricks Proficiency: Strong knowledge of Spark (SQL, PySpark) for data transformation and processing within Databricks, along with experience building workflows and automation using Databricks Workflows.
Azure Data Services: Hands-on experience with Azure services like Azure Data Lake, Azure Blob Storage, and Azure Synapse for data storage, processing, and publication.

Data Governance & Security: Familiarity with managing data governance and security using Databricks Unity Catalog, ensuring data is appropriately organized, secured, and accessible to authorized users.
Optimization & Performance Tuning: Proven experience in optimizing data pipelines for performance, cost-efficiency, and scalability, including partitioning, caching, and tuning Spark jobs.
Cloud Architecture & Automation: Strong understanding of Azure cloud architecture, including best practices for infrastructure-as-code, automation, and monitoring in data environments