Back
Mid-level, common data-engineer title with moderate brand and broad requirements increases competition.
Role requires Databricks, Delta Lake and Azure expertise, limiting easy cross-industry interchangeability.
Explicit 5–8 years plus mandatory Databricks, Azure, Spark, Unity Catalog and pipeline experience.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and maintain scalable ETL/ELT pipelines on Azure Databricks following medallion architecture ensuring reliability, performance, and timely data delivery.
Manage Unity Catalog for data governance including access control, data classification, and lineage; implement audit and operational metadata systems for pipeline observability and reconciliation.
Enable data sharing and migration across cloud platforms; orchestrate and automate data workflows with CI/CD practices; deliver curated datasets for analytics and AI/ML consumption.
Bachelor's degree in IT, Computer Science, Data Science, Analytics, or Statistics.
5–8 years of hands-on experience in data engineering or analytics engineering with substantial Databricks delivery experience.
Strong expertise in Azure Databricks components (workspaces, clusters, Unity Catalog, ADLS Gen2), Spark/PySpark, SQL, and Delta Lake medallion architecture pipelines.
Experience with cloud data ecosystem integration (AWS, GCP, Snowflake, Redshift, BigQuery), data governance, and orchestration tools (Databricks Workflows/Jobs, DLT) including CI/CD with Git.
Experienced senior-level data engineer with deep technical skills in Azure Databricks and comprehensive pipeline design for diverse data types using medallion architecture.
Proficient in implementing data governance frameworks and operational metadata to support enterprise data observability, audit and compliance needs.
Familiarity with AI/ML enablement infrastructure on Databricks, cross-cloud data sharing, and ability to deliver analytics-ready datasets integrated with BI tools like Power BI or Tableau.