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Mid-level generalist cloud-data role in metro location with common tech requirements increases applicant competition.
Cloud data engineering skills are highly transferable across industries, so background sensitivity is low.
Explicit 5+ years and mandatory Databricks/Azure/AWS/Spark/Python experience raises shortlisting strictness.
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Build, support, and maintain scalable cloud-based data solutions across Azure and AWS using Databricks, Spark, and related technologies.
Develop data ingestion and transformation pipelines employing Spark, PySpark, SQL, Python, and ETL/ELT patterns including API-based ingestion from diverse platforms.
Collaborate with architecture, security, FinOps, analytics, and business teams to enable cost-effective, secure, and reliable cloud data delivery supporting reporting, AI, and operational decisions.
Bachelor's degree in Computer Science, IT, Data Engineering, Information Systems, or related field (or equivalent experience).
At least 5 years of experience in cloud engineering, data engineering, systems integration, or related technical roles.
Hands-on experience with Databricks, Spark, SQL, Python, data transformation pipelines, and REST API development or consumption.
Experience building or supporting cloud-native solutions in Microsoft Azure and Amazon Web Services (AWS).
Experienced in both Azure and AWS cloud environments with extensive use of Databricks and Spark for data pipelines.
Strong background in data platform engineering involving API integration, cloud security, FinOps, and automated data solutions.
Able to work cross-functionally in a matrixed environment with accountability for engineering standards, governance, and cost management.