Blog

1. Key Responsibilities:

• Design, develop, and maintain data pipelines and ETL processes using Databricks, Spark, and other data engineering tools.
• Implement and report on business key performance indicators (KPIs) in data warehousing environments.
• Utilize analytic SQL to work with traditional relational databases and distributed systems.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
• Optimize data workflows for performance, scalability, and reliability.
• Develop and maintain documentation for data engineering processes and solutions.
• Ensure data quality and integrity through rigorous testing and validation.
• Provide technical guidance to a team of developers to enhance their capabilities and productivity.
• Conduct comprehensive code reviews and oversee quality assurance processes.
• Participate in technical discussions as a consultant for feasibility studies, technical alternatives, and risk assessments.
• Collaborate with stakeholders to define project scope, objectives, deliverables, and submit status reports.

2. Preferred Qualifications / Skills and Competencies:

• 4+ years of experience with key performance indicators (KPIs) in data warehousing.
• 4+ years of experience in analytic SQL, working with traditional relational databases and distributed systems.
• 3+ years of experience with Python, PySpark, and data orchestration/ETL tools.
• 2+ years of experience with Databricks/Spark, Power BI, and Azure.
• 2+ years of experience with X12 EDI, particularly in the healthcare domain.
• Strong understanding of data warehousing concepts and best practices.
• Expertise in SQL, relational databases, and distributed systems.
• Knowledge of X12 EDI standards.
• Excellent problem-solving, communication, and collaboration skills.
• Experience with process compliance, performance optimization, and coding standards.