Back
Tier-1 brand, popular mid-level Databricks data engineer role, and generalist skillset drive high competition.
Requires Databricks, Spark, cloud, and financial-regulations expertise, limiting cross-industry transferability.
Multiple mandatory Databricks, Spark, cloud, and governance requirements enforce high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy scalable batch and real-time data pipelines using Python, PySpark, Spark SQL, and Databricks.
Deploy and maintain Databricks workspaces on AWS or GCP, including secure integrations with cloud storage, access controls, and secrets management.
Lead technical standards, perform code reviews, mentor junior engineers, and manage CI/CD pipelines for data engineering artifacts.
Strong proficiency with Python and advanced SQL including query optimization.
Minimum 3 years experience developing in Databricks with deep knowledge of Delta Lake and Unity Catalog.
Hands-on experience deploying Databricks on AWS or GCP including cloud-native components such as S3, IAM, and serverless query engines.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced technical leader with SME expertise in distributed computing and cloud-native data pipeline architecture.
Demonstrated ability to optimize Spark clusters and manage complex data governance and compliance requirements (e.g. BCBS 239).
Proficient in integrating data engineering with data science and BI teams in Agile/Scrum environments.