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Popular data-engineer role in metro with common tech stack yields medium candidate competition.
Core data engineering skills are transferable, though healthcare/fintech preference increases domain sensitivity.
Explicit 7+ years and mandatory DBT/Airflow/Databricks/PySpark requirements create high shortlisting rigor.
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Own end-to-end development and implementation of orchestrated data pipeline solutions to enable accurate data-driven decision-making.
Optimize and automate data processing workflows using DBT, Airflow, Azure Databricks, Py-spark, SQL, Python with CI/CD integration.
Collaborate with cross-functional teams to understand data needs, ensure data quality, and provide ad hoc data analysis and reporting support.
Bachelor’s degree in Computer Science, Information Technology, or related field.
Minimum 3+ years of hands-on experience with DBT, Airflow, Azure Databricks, Py-spark, SQL, Python, and Automation.
Experience with cloud platforms such as Azure, AWS, or GCP.
Strong debugging, automation skills, and understanding of Data Warehouse/Data Lake concepts.
7+ years of hands-on experience specifically with DBT, Airflow, Azure Databricks, Python, Py-spark, and SQL, preferably in Healthcare or Fintech domains.
Expertise in building robust, high-performance data pipelines with an automation-first mindset.
Ability to influence technical decisions by recommending appropriate data solutions and ensuring data quality and compliance.