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Metro location, broad cloud/Databricks skillset, and a commonly targeted senior cloud role increase competition.
Core cloud and data engineering skills are broadly transferable across industries despite FinOps context.
Explicit 8+ years, required Databricks/Azure/AWS skills and certifications imply strict screening.
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Design, build, and support scalable cloud data solutions on Azure and AWS, focusing on Databricks, data transformation, and API integration.
Lead development and improvement of ETL/ELT pipelines, lakehouse architectures, and automation using Spark, PySpark, SQL, Python, and Infrastructure as Code.
Provide technical leadership and mentorship while ensuring solutions are secure, governed, cost-effective, and enterprise-ready across cross-functional teams.
Bachelor's degree in Computer Science, IT, Data Engineering, or related field (or equivalent experience).
8+ years of experience in cloud engineering, data engineering, or systems integration roles.
Hands-on experience with Databricks, Spark, PySpark, SQL, Python, Azure services (Databricks, Data Factory, Storage, Functions, Vault), AWS services (S3, Lambda, Glue, Athena), and Infrastructure as Code (Terraform, Bicep, ARM templates, CloudFormation).
Databricks, Microsoft Azure, AWS, or data engineering certifications required. Notice period: Not explicitly mentioned.
Experienced working in matrix and hybrid environments requiring collaboration with IT, Finance, Architecture, and business teams.
Strong expertise in cloud-native platforms with focus on enterprise-scale data transformations and secure API ingestion patterns.
Proficient with CI/CD, automation, monitoring, and governance to enhance platform reliability and operational efficiency.