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Medium—Tier-1 brand and mid-level data role attract applicants despite specialized Databricks/Azure requirements.
High—role requires specific Azure Databricks, Delta Lake, and streaming experience, limiting cross-industry transferability.
High—explicit 7+ years requirement plus mandatory Databricks, PySpark, Azure, and Kafka skills.
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Design and implement high-throughput, event-driven architectures for near real-time data pipelines on Azure and Databricks Lakehouse.
Build and optimize cloud-native batch and streaming data pipelines, including configuring Unity Catalog with Bronze/Silver/Gold medallion architecture for governance and lineage.
Lead engineering excellence through standardized DevOps practices, mentor engineers, and deliver reusable blueprints while collaborating with domain teams.
7+ years software engineering experience focusing on data infrastructure and backend systems.
Hands-on experience with Microsoft Azure data platform components including Azure Data Factory, Event Hubs, Blob Storage, ADLS Gen2, and Azure DevOps.
Production experience with Azure Databricks, Delta Lake, and PySpark for distributed data processing.
Experience with streaming frameworks such as Kafka or comparable technologies for real-time data pipelines.
Experienced in designing and operating large-scale data platforms leveraging Lakehouse and Delta Lake architectures on cloud native environments.
Proficient in implementing and optimizing streaming data pipelines and batch workloads with strong expertise in SQL, Python, and distributed processing.
Skilled in driving technical decisions and collaborating with global teams and stakeholders, with a focus on engineering best practices and scalable data ingestion for AI and analytics use cases.