Basic Qualifications
Bachelors degree in Computer Science, Engineering, Data Science, or equivalent practical experience.
6+ years of data engineering experience in designing, implementing, and optimizing large-scale data systems.
Strong proficiency in Python, with production-level experience in building reusable, scalable data pipelines.
Hands-on expertise with Databricks (Delta Lake, Spark, MLflow), and modern orchestration frameworks (Airflow, Prefect, Dagster, etc.).
Proven track record of deploying and supporting AI/ML pipelines in production environments.
Experience with cloud platforms (AWS, Azure, or GCP) for building secure and scalable data solutions.
Familiarity with regulatory compliance and data governance standards in healthcare or life sciences.
Preferred Qualifications
Experience with event-driven systems (Kafka, Kinesis) and real-time data architectures.
Strong background in data modeling, Lakehouse/lake/warehouse design, and query optimization.
Exposure to AI orchestration platforms and generative AI use cases. Contributions to open-source projects or published work in data engineering/ML.
Agile development experience, including CI/CD, automated testing, and DevOps practices.

Keyskills: Data Science Artificial Intelligence Cloud Aiml Machine Learning Deep Learning Python
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