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Strong Tier-1 brand, mid-level data engineer manager title, and broad sought-after tech stack increase competition significantly.
Core data engineering skills are transferable across industries, though healthcare data governance adds moderate specialization.
Explicit 6+ years requirement plus specific Databricks/PySpark/Kafka/Azure skills and degree make shortlisting stringent.
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Lead development and optimization of batch and real-time data pipelines using Python, PySpark, Databricks, and Kafka, including implementing Medallion architecture.
Ensure data reliability, governance, monitoring, and lineage; drive architecture decisions, platform modernization, and Azure cloud integration.
Mentor engineers, conduct design/code reviews, define best practices, and collaborate with stakeholders and AI/Data Science teams to deliver analytics-ready data solutions.
Bachelor's degree in Computer Science, Engineering, Information Technology, or related field.
6+ years of hands-on experience in data engineering, data processing, or similar technical roles.
Experience with Apache Kafka, Databricks, PySpark, and Microsoft Azure ecosystem (e.g., Azure Data Factory, Azure Data Lake, Azure Synapse).
Solid SQL skills, data modeling, ETL/ELT processes, and knowledge of distributed data processing and big data architectures.
Experienced in leading and optimizing large-scale data engineering projects using modern cloud and big data technologies.
Skilled at mentoring and setting engineering standards in a fast-paced environment with complex stakeholder collaboration.
Familiar with advanced cloud data platforms (Azure and Snowflake), CI/CD practices, and data pipeline modernization initiatives.