Data Engineer I

The Data Engineer Level I is focused on supporting the design, development, and maintenance of data systems and pipelines under the guidance of senior engineers. Assists in the collection, processing, and storage of data from various sources to ensure its availability for analysis and reporting. Supports data validation and maintains data quality within the data lake and data warehouse.

Key Responsibilities:

• Data Management: • Assists in developing and maintaining simple ETL processes. • Supports building data pipelines to collect and move data from source systems to storage platforms. • Helps with data cleaning and preprocessing to ensure high data quality. • Monitors and troubleshoots data pipeline performance and address issues as they arise. • Maintains system logs and ensures the smooth operation of data processes. • Assists with maintaining and upgrading existing data infrastructure, including databases and data warehouses.

Collaboration:

Works closely with senior data engineers and other teams to understand data needs and requirements.

Documentation:

Documents data workflows, pipelines, and technical processes. Maintains clear and organized records for data sources and transformations.

Education:

Associates or Bachelors degree in Computer Science, Information Technology, Mathematics, or a related field; or the equivalent of 4 years practical experience in a comparable job. Microsoft Developer certification a plus, but not required.

Experience:

2 - 3 years of experience in a data engineering, data analysis, data warehouse development or related technical role. Healthcare experience desired, especially Epic (Electronic Health Record system).

Technical Skills:

• Good understanding of data warehouse concepts and how they apply to data integration.
• Knowledge of programming languages such as Python, SQL.
• Familiarity with relational databases like SQL Server.
• Strong understanding of SQL for querying and manipulating relational databases.
• Exposure to data processing tools and frameworks - both on-premises and cloud based. (PySpark, ETL tools like SSIS, Azure data Factory etc.)

Communication & Teamwork:

• Ability to collaborate effectively with cross-functional teams, such as data scientists, analysts, and senior engineers.
• Strong attention to detail and ability to follow standard operating procedures.

Desirable Skills:

• Familiarity with cloud-based platforms like Databricks (AWS, Azure, GCP).
• Willingness to learn and develop skills in data engineering technologies.