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Protocol Intelligence
Data-driven signals on your job's competitivenessCommon mid-level data engineer demand but Databricks specialization reduces applicant pool.
Databricks-specific skills moderately restrict transferability despite general data engineering portability.
Explicit 4-6 years plus mandatory Databricks, Spark, Python, and cloud experience increases strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain data pipelines and cloud data solutions on Databricks platform.
Develop PySpark and SQL code for data ingestion, transformation, and loading, supporting routine workflows and troubleshooting data pipeline issues.
Collaborate with data engineers and product teams while learning and applying Databricks features like Delta Live Tables and SQL Analytics.
Minimum Requirements
4-6 years experience in software or data engineering with exposure to cloud-based environments.
Hands-on experience with Databricks platform, Apache Spark, PySpark, Python, and SQL.
Bachelor's degree in Computer Science, Engineering, or related quantitative field.
Experience with version control systems (Git) and some exposure to a major cloud platform (AWS, Azure, or GCP).
Ideal Candidate Profile
Intermediate to senior-level engineer comfortable with modern data engineering stacks focused on Databricks and Spark.
Experience working closely under senior guidance, showing ability to develop, maintain, and optimize data pipelines with minimal supervision.
Proactive in learning new data engineering features and cloud tools and integrating them into workflows, with practical understanding of data warehousing and storage formats.