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Tier-1 employer and broad data-engineering stack increase competition, seniority (12+ years) slightly lowers applicant density.
Core data engineering skills are highly transferable across industries despite healthcare domain context.
Requires 12+ years, specific Azure/Databricks/Spark stack and proven leadership, making filters very strict.
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Lead design and implementation of scalable data engineering solutions using Azure, Databricks, Spark, and related technologies.
Manage large-scale data migration and modernization programs, ensuring alignment with business requirements and delivery within timelines.
Drive engineering excellence through mentoring teams, establishing coding standards, and optimizing platform performance, reliability, and cost.
Bachelor's degree in Computer Science, Engineering, or related technical field.
12+ years of professional experience in data engineering, data products development, and AI tools with technical leadership roles.
Hands-on experience with Azure, Apache Spark, Databricks (Scala and Python), Azure Data Factory, SQL and NoSQL databases.
Familiarity with Agile methodologies, unit testing, test automation, version control (Git), and solution architecture with robust data security measures.
Experienced leader capable of managing large-scale data migration and modernization programs with strong stakeholder management skills.
Skilled in designing and optimizing batch and streaming data solutions with expertise in cloud data platforms and AI/GenAI technologies.
Proven ability to mentor data engineering teams and enforce engineering standards to achieve business outcomes within committed timelines.