Azure Databricks Data Engineer

Job Description:

• We are looking for an enthusiastic Databricks Data Engineer to architect, develop, and enhance contemporary data platforms utilizing Databricks and cloud technologies.
• The successful candidate will possess practical experience in constructing large-scale data pipelines, executing Lakehouse frameworks, and facilitating advanced analytical solutions through Databricks Spark and cloud infrastructures.
• In this position, you will collaborate with data architects, analysts, business stakeholders, and engineering teams to provide scalable, high-performance data solutions that underpin enterprise-wide digital transformation efforts.

Key Responsibilities:

• Design, develop, and sustain scalable data pipelines utilizing Databricks and Apache Spark.
• Create and enhance ETL/ELT workflows for processing extensive structured and unstructured datasets.
• Build data ingestion frameworks for integrating data from various sources, including databases, APIs, applications, and cloud storage.
• Implement and oversee Lakehouse architecture using Databricks and Delta Lake.
• Optimize Spark jobs and Databricks workloads for performance, scalability, and cost-effectiveness.
• Work collaboratively with analytics, BI, and business teams to comprehend data requirements and deliver reusable data assets.
• Establish data quality frameworks, monitoring solutions, and governance controls throughout data pipelines.
• Diagnose production issues and ensure timely resolutions to maintain business continuity.
• Engage in architecture reviews, code reviews, and technical discussions to advocate for best practices.
• Develop and maintain technical documentation, reusable components, and deployment standards.

Technical Requirements:

• 11 years of substantial hands-on experience with Databricks.
• Expertise in PySpark or Spark SQL for distributed data processing.
• Strong SQL capabilities, including experience in query optimization and performance enhancement.
• Proven experience in designing and developing ETL/ELT pipelines.
• Hands-on knowledge of Delta Lake DataFrames and Spark architecture.
• Understanding of data warehousing concepts, dimensional modeling, and data lake architectures.
• Experience with cloud platforms like Azure, AWS, or GCP.
• Familiarity with Git-based source code management and DevOps methodologies.
• Strong analytical, debugging, and problem-solving abilities.

Additional Responsibilities:

• The Infosys Data Analytics practice assists organizations in modernizing their data landscapes through cloud-native architectures, advanced analytics, AI, and intelligent data platforms.
• As a Databricks Data Engineer, you will engage in enterprise-level data modernization initiatives, including Lakehouse implementations, cloud migrations, real-time analytics, and AI-ready data ecosystems.

Preferred Skills:

Technology->Data Engineering->Databricks