Seeking a seasoned Databricks Developer with 9-12 years of experience to design, construct, and maintain advanced Data Lakehouse solutions. This role requires strong practical skills in Databricks, PySpark, Delta Lake, cloud technologies, and large-scale data processing. You will be instrumental in building robust data pipelines, optimizing distributed data workloads, and driving AI-focused initiatives.

This opportunity offers the chance to work on cutting-edge data modernization and migration projects, contributing significantly to enterprise-level data strategies. The position emphasizes hands-on development and problem-solving within a dynamic engineering environment.

Key responsibilities include designing, developing, and maintaining scalable data engineering solutions using Databricks. You will build and optimize batch and real-time data processing pipelines, develop ETL/ELT frameworks with PySpark and Spark SQL, and implement Delta Lake architecture with a focus on data governance.

Further duties involve processing extensive structured, semi-structured, and streaming datasets, optimizing Spark jobs for peak performance, and creating reusable frameworks, notebooks, and workflows. Collaboration with cross-functional teams, including data scientists and architects, is crucial for delivering enterprise-grade solutions. This role also encompasses implementing monitoring, alerting, security, and compliance measures, participating in code reviews, and mentoring junior engineers.

Candidates should possess a Bachelor's or Master's degree in a relevant technical field. A minimum of 9-12 years of IT experience is required, with at least 4 years dedicated to hands-on Databricks and Spark development. Essential skills include profound expertise in distributed data processing, Lakehouse architecture, and building enterprise-scale data engineering solutions.

Strong proficiency in PySpark, Spark SQL, and performance optimization techniques is mandatory. Experience with Agile development methodologies and exceptional analytical, troubleshooting, and communication skills are also critical. Preferred qualifications include experience with cloud-native analytics platforms on Azure, AWS, or GCP, and knowledge of machine learning workflows within Databricks.