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Known employer, mid-level requirement, and metro location with general SRE/Data skills driving high candidate competition.
Role's Databricks, PySpark, and Medallion architecture focus moderately limits cross-industry transferability.
Explicit 5-year requirement plus mandatory Databricks, PySpark, and Azure skills increases screening strictness.
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Own post-deployment health, latency, and performance of productionized AI applications and Medallion architecture data pipelines.
Develop and maintain automated monitoring dashboards and self-healing mechanisms to minimize manual intervention and ensure operational reliability.
Collaborate with Feature Teams to optimize PySpark jobs, conduct root-cause analysis, and enforce production readiness standards.
5 years of relevant work experience.
Hands-on experience with Azure Data Engineering Stack including Databricks, ADLS Gen2, Azure Data Factory, and Azure DevOps.
Proficiency in Python and PySpark for performance optimization of data pipelines and AI applications.
Experience working with Medallion architecture and production environment monitoring and incident management.
Strong problem-solving skills focused on debugging and reverse-engineering complex data systems and AI models in production.
Experienced in balancing proactive engineering automation and reactive operational reliability in cloud-based AI and data pipeline environments.
Skilled at collaborating across teams to implement production readiness measures and optimize cost and compute efficiency in Azure Data Engineering ecosystems.