Data Engineer I, II

Moda Health•Portland, OR

16d•$78,911 - $110,265•Remote

About The Position

The Data Engineer role on the Data Science Team (DST) is responsible for designing, maintaining, and enhancing data integration pipelines that support the Enterprise Data Warehouse (EDW) and downstream analytics. This role supports the existing on premises data environment while actively contributing to cloud modernization initiatives, including the adoption of modern data transformation and cloud data warehouse technologies such as dbt and Snowflake. This is a FT WFH position. Pay Range $78,911.43 - $110,265.00 annually (depending on experience). This role may be classified as hourly (non-exempt) depending on the applicant's location. Actual pay is based on qualifications. Position level is based on qualifications & experience. Applicants who do not exceed the minimum qualifications will only be eligible for the low end of the pay range Please fill out an application on our company page, linked below, to be considered for this position. https://j.brt.mv/jb.do?reqGK=27785806&refresh=true

Requirements

Bachelor's Degree in a related field or equivalent practical experience preferred
1 - 5 years of experience working with large datasets in relational databases and SQL-based data analytic platforms.
Experience with SQL Server and modern SQL-based cloud data warehouses, e.g. Snowflake, BigQuery, Redshift, etc.) is preferred.
1 - 5 years of hands-on SQL development experience, including writing complex queries, stored procedures, and performance tuning.
1 - 5 years of practical experience developing and supporting data pipelines and transformations using SSIS, SQL Agent, Azure Data Factory, and/or Airflow.
Exposure to modern cloud-based transformation tools such as dbt or similar is preferred.
Experience supporting on-premises data warehouse environments and contributing to future cloud modernization efforts, including assisting with migrations from on-premises SQL Server–based platforms to Snowflake or similar cloud technologies.
Solid understanding of data management principles, relational data structures, and data modeling, with the ability to apply best practices in both on-premises and modern SQL-based cloud data warehouses.
Demonstrated analytical skills and ability to contribute to data driven solutions that address real business problems, with guidance from senior engineers when needed.
Working knowledge of Agile development methodologies and participation in collaborative, team-based delivery models.
Exposure to DevOps practices using tools such as Azure DevOps (CI/CD, work item tracking, GIT version control, Pull Requests) is desirable.
Experience working with business intelligence and reporting tools such as SAS Enterprise Guide, Power BI or SSRS, including supporting downstream analytics and reporting users.
Familiarity with programming or scripting languages such as Python, R, or Java is a plus.
Experience with healthcare data is strongly preferred.

Nice To Haves

Experience in healthcare systems is a plus; Facets experience preferred.

Responsibilities

Design, develop, test, and maintain ETL/ELT pipelines that ingest data from multiple internal and external source systems into the Enterprise Data Warehouse (EDW).
Automate, maintain and enhance existing data pipelines to ensure reliability, performance, and scalability, while implementing incremental improvements as part of ongoing cloud modernization efforts.
Contribute to data platform modernization initiatives, including assisting with migrations to cloud-based infrastructure and adoption of modern data tools and practices.
Develop, implement, and support data quality checks, validation processes, and monitoring to ensure data accuracy and consistency.
Build and support modern, enterprise-grade data pipelines and models under the guidance of senior engineers to enable adoption of next-generation data architecture.
Collaborate with cross-functional teams to understand data and reporting requirements and provide ongoing support for data infrastructure needs.
Support the Data Science team by implementing and maintaining data solutions that enable advanced analytics and machine learning cases.
Participate collaboratively in Enterprise Data Warehousing, Business Intelligence, and other data management initiatives, including code reviews and documentation.
Perform other duties as assigned.