Back
Tier-1 brand, mid-level generalist role, metro hiring, and broad Azure/Databricks skillset increase competition.
Cloud-focused Azure Databricks skills are transferable but favor data-centric or cloud-centric employers.
Explicit 3–8 years requirement plus mandatory Azure, Databricks, and ADF skills make filters strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design and development of cloud-based data engineering solutions using Microsoft Azure services, including data pipelines, data integration, and data warehousing.
Provide technical and thought leadership in data access, processing, modeling, visualization, and analytics within the Analytics Practice.
Collaborate with clients to translate business requirements into technical specs, develop reusable code and best practices for data warehousing and ETL in an Agile/DevOps environment.
3-8 years of experience in data engineering or related field.
Proven expertise with Microsoft Azure data services (Azure Data Factory, Azure Synapse, Azure SQL, Azure Data Lake, Azure Cosmos DB).
Hands-on experience with Apache Spark (Python and/or Scala), Azure Databricks, and building data pipelines.
Bachelor’s or Master’s degree in Engineering, MCA, MBA, or related field; certifications in Azure/Databricks are a plus.
Experienced working in Agile/DevOps teams delivering cloud-based analytics solutions with focus on Azure ecosystem.
Strong technical leadership capability in designing scalable, high-performance data pipelines and cloud data architectures.
Familiarity with CI/CD, infrastructure as code, and data warehouse modeling principles (e.g., Kimball methodology).