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Mid-level data role requiring Azure Databricks specialization increases applicant competition moderately.
Strong Azure Databricks and data platform focus limits cross-industry transferability.
Mandatory 5+ years and required Azure/Databricks expertise create strict screening filters.
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Design, develop, and maintain scalable, automated data integration and pipeline solutions using Microsoft Azure technologies (Data Factory, Synapse, Databricks).
Lead the architecture and implementation of Databricks-based lakehouse data platforms including performance tuning of Spark jobs.
Build and manage application and API integrations using Azure Functions, Logic Apps, Service Bus, Event Grid, and Azure API Management ensuring secure, reliable, and documented data flow.
5+ years experience in Data Engineering or Data Platform development.
3+ years hands-on experience with Azure Databricks.
Strong proficiency in Python (PySpark), SQL, and Azure data services including Data Factory, Data Lake Storage Gen2, Synapse Analytics.
Experience in building enterprise-scale data pipelines and application/API integrations using Azure Functions, Logic Apps, and Azure API Management.
Experienced data engineer with demonstrated ability to design and deliver end-to-end Azure data integration architectures that support enterprise scale and 99.9% uptime requirements.
Technically strong in Spark optimization, data modelling (including medallion architecture), and API design with secure authentication on Azure platform.
Comfortable driving technical solutions in evolving environments and collaborating across business and technical teams to translate requirements into well-documented, scalable data solutions.