Databricks Data Engineer

Roles & Responsibilities


Job Title: Databricks Data Engineer

Job Description:

As a Databricks Data Engineer, you will be responsible for designing, developing, and maintaining data solutions for data generation, collection, and processing in Big Data environment using predominantly PySpark/Python. Your typical day will involve creating data pipelines, ensuring data quality, and implementing ETL processes to migrate and deploy data across systems using PySpark.


Roles & Responsibilities:
• Collaborate closely with data scientists, data engineers, and business stakeholders to gather requirements and understand the business objectives driving data pipeline development.
• Design, develop, and maintain robust, scalable high-performance Data Pipelines using Databricks.
• Leverage Databricks features such as Lakehouse and Delta Lake for efficient data storage and Spark for distributed processing
• Develop ETL/ELT pipeline using Databricks
• Monitor pipeline health, troubleshoot data issues
• Migrate on Prem Pyspark, SAS data pipeline and ML Models to Databricks
• Define and implement best practices in Databricks
• Evaluate new Databricks features and tools, helping the organization stay at the forefront of innovation in AI and Big Data
• Collaborate with cross-functional teams to identify and resolve data-related issues.


Qualifications:
• Proven expertise in implementing Lakehouse and Delta Lake using Databricks.
• Strong PySpark and Python experience
• Databricks Certified Data Engineer Professional Certification
• Familiarity with ML Ops/LLM Ops and distributed systems.
• Experience with Big Data platform like Cloudera Hadoop and Could platforms like AWS, GCP.
• Solid understanding of system design patterns, scalability, observability, and performance tuning.
• Strong analytical and problem-solving skills.
• Passion for exploring and building with emerging technologies.


Good to Have Skills:
• AWS EKS Experience, Dockers and Containers

Salary Range: $125,000 to $140,000 per year