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Mid-level experience and metro location present moderate applicant competition.
Role requires specific data platform and streaming expertise, so industry transfers are moderately constrained.
Explicit 5.5+ years and mandatory Databricks/Spark/Kafka/Terraform skills make filters strict.
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Design, develop, and maintain scalable batch, near real-time, and streaming data pipelines on Azure Databricks using Python, SQL, Apache Spark, and Kafka.
Automate deployment and infrastructure provisioning via Terraform, GitHub Actions, and CI/CD pipelines to ensure robust data platform operations.
Collaborate cross-functionally to monitor, troubleshoot, optimize data pipelines and mentor junior engineers, enhancing data platform reliability and engineering best practices.
5.5+ years of total professional experience.
Strong hands-on skills in Python, SQL, and Apache Spark programming.
Expertise in Databricks, Apache Kafka, Terraform, and cloud platforms preferably Azure Databricks.
Bachelor’s or master’s degree in computer science, Information Technology, or a related field.
Experienced in building and operationalizing complex data ingestion and transformation pipelines in cloud environments using metadata-driven engineering standards.
Demonstrates proficiency with infrastructure as code, CI/CD pipelines, and modern DevOps practices.
Familiar with streaming technologies, data governance, and adoption of AI/ML innovations within scalable cloud data platform contexts.