Enterprise Data Engineer

Avera•Sioux Falls, SD

4d•Onsite

About The Position

The Data Engineer is focused on supporting the design, development, and maintenance of data systems and pipelines under the guidance of senior engineers. This position involves assisting in the collection, processing, and storage of data from various sources to ensure its availability for analysis and reporting. The role also includes supporting data validation and maintaining data quality within the data lake and data warehouse.

Requirements

Bachelor's in computer science, analytics, mathematics, business, or related fields or an equivalent combination of education and work experience.
At least 2-3 years of relevant experience.
Knowledge of programming languages such as SQL and Python
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, SSIS, Azure Data Factory, etc.)
Ability to collaborate effectively with cross-functional teams, such as data scientists, analysts, and other engineers.
Strong attention to detail and ability to follow standard operating procedures.
Familiarity with cloud-based platforms like Databricks (AWS,Azure,GCP).
Willingness to learn and develop skills in data engineering technologies.

Nice To Haves

Heath care experience, with preference to Epic Electronic Health Record system.

Responsibilities

Design and develop data systems such as data warehouse and data lake.
Assist in developing and maintaining ETL processes.
Support building data pipelines to collect and move data from source systems to storage platforms.
Help with data cleaning and preprocessing to ensure high data quality.
Monitor and troubleshoot data pipeline performance and address issues as they arise.
Maintain system logs and ensure the smooth operation of data processes.
Assist with maintaining and upgrading existing data infrastructure, including databases and data warehouses.
Work closely with data scientists, analysts, and business stakeholders to understand data requirements and provide solutions.
Document data workflows, pipelines, and technical processes.
Maintain clear and organized records for data sources and transformations.