Staff Data Engineer

Position Overview

We seek a results-oriented Data Engineer with a minimum of 6+ years of experience in data pipeline development within cloud environments. The successful candidate shall be responsible for designing, constructing, and optimizing Azure-based data ingestion and transformation pipelines using PySpark and Spark SQL. This role requires collaboration with cross-functional teams to deliver high-quality, reliable, and scalable data solutions.

Duties and Responsibilities

• Design, develop, and maintain high-performance ETL/ELT pipelines using PySpark and Spark SQL using cloud-native components in Databricks
• Build and orchestrate data workflows in AZURE.
• Implement hybrid data integration between on-premise databases and Azure Databricks using tools such as ADF, HVR/Fivetran, and secure network configurations.
• Enhance/optimize Spark jobs for performance, scalability, and cost efficiency.
• Implement and enforce best practices for data quality, governance, and documentation.
• Collaborate with data analysts, data scientists, and business users to define and refine data requirements.
• Support CI/CD processes and automation tools and version control systems like Git.
• Perform root cause analysis, troubleshoot issues, and ensure the reliability of data pipelines.

Qualifications

Required Qualifications

• Bachelors degree in Computer Science, Engineering, or related field.
• 6+ years of hands-on experience in data engineering.
• Proficiency in PySpark, Spark SQL, and distributed processing.
• Strong knowledge of Azure cloud services including ADF, Databricks, and ADLS.
• Experience with SQL, data modeling, and performance tuning.
• Familiarity with Git, CI/CD pipelines, and agile practices.

Preferred Qualifications

• Experience with orchestration tools such as Airflow or ADF pipelines.
• Expertise in Databricks, Delta Lake, Unity Catalog and Azure Data Services
• Knowledge of real-time streaming tools (Kafka, Event Hub, HVR).
• Exposure to APIs, data integrations, and cloud-native architectures.
• Familiarity with enterprise data ecosystems (S/4 HANA, BDC)