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We're seeking a Data Engineer to design and implement data pipelines that power analytical capabilities. This hands-on role requires an understanding of data engineering best practices and the ability to translate business requirements into technical solutions. You will be part of a dedicated team creating datasets for analytic and data science workloads.
Job Responsibility:
Design and build ETL/ELT data pipelines to ingest, process, and transform datasets from multiple sources
Implement best practices for performance tuning, partitioning, and clustering to optimize data queries
Follow data quality standards, data governance frameworks, and security policies for data storage and access
Develop and optimize data models and schemas to support analytics, reporting, and machine learning requirements
Collaborate with data scientists and analysts to design data solutions that integrate with BI tools and machine learning models
Create comprehensive documentation for data pipelines, workflows, and processes
Requirements:
2+ years of applicable work experience
Proficiency in Python, specifically with ETL pipelines
Strong proficiency in SQL and experience in developing complex queries
Experience deploying data pipelines in a cloud environment (any of Azure, AWS, GCP)
Excellent communication and interpersonal skills, with the ability to collaborate effectively with data scientists and analysts
College degree or certification in related fields
Nice to have:
Experience working with healthcare data, especially Epic
Experience using GCP's BigQuery
Knowledge of data governance best practices in a cloud environment
Experience working with Machine Learning processes
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