Back
Tier-1 brand and generic Data Engineer title but Databricks specialization reduces applicant density.
Databricks and cloud skills transfer broadly, but financial services governance raises domain specificity.
Multiple mandatory Databricks, cloud, and programming requirements plus financial compliance increase filter strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Manage and execute large-scale Data Engineering projects using Databricks platform on Azure and AWS clouds.
Implement and optimize Databricks components including Notebooks, Clusters, Jobs, Delta Lake, Unity Catalog, and configure RBAC.
Develop, debug, and optimize data processing pipelines using Scala, Python, PySpark, SQL, and implement CI/CD pipelines with Azure DevOps.
Expertise in Databricks platform (Notebooks, Clusters, Jobs, Delta Lake) and Unity Catalog with RBAC configuration.
Proficient with Azure Cloud services, specifically Azure Data Factory, Blob Storage, Azure Databricks, and CI/CD using Azure DevOps.
Strong programming skills in Scala, Python, PySpark, and SQL with Apache Spark optimization experience.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in managing complex, large-scale data engineering implementations on multi-cloud (Azure and AWS) environments.
Skilled in secure coding practices, data governance, and compliance standards related to financial services.
Capable of independently debugging and optimizing PySpark and SQL code with strong operational ownership over the data pipelines.