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Tier-1 brand, mid-level seniority and metro role boost competition, stack specificity moderates it.
Data engineering skills transfer across industries but Azure Databricks and streaming specialization increases domain sensitivity.
Explicit 7+ years and mandatory Databricks, PySpark, Azure, and Kafka create strict filtering.
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Lead design and implementation of high-throughput, event-driven data pipelines integrating Azure and Databricks Lakehouse environments.
Build and optimize cloud-native batch and streaming data ingestion frameworks with centralized governance and end-to-end lineage.
Drive engineering excellence by standardizing DevOps practices, optimizing data engineering code, and mentoring engineers across domain teams.
7+ years of software engineering experience focused 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 for orchestration and CI/CD.
Production experience with Azure Databricks, Delta Lake, and PySpark for distributed data processing and transformation.
Experience with streaming frameworks such as Kafka or comparable for real-time data processing including CDC and incremental batch patterns.
Experienced in data platform architecture specifically Lakehouse, Delta Lake, and Azure Databricks with strong SQL and Python skills.
Able to influence senior stakeholders and lead global technical decisions in large enterprise environments.
Has worked on cloud-native tools and prioritizes engineering automation, reusable blueprints, and performance optimization.