Data Engineer — Cloud ETL & Analytics for Federal Health

Metas Solutions; Myrtle Point (OR); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

Medical, Dental, Vision

401K with company match

PTO and growth opportunities

Job summary

Metas Solutions is seeking a Data Engineer to design, build, and maintain cloud-based data pipelines and stores to enable analytics across large datasets. You will craft scalable ETL/ELT workflows, structure data storage architectures, and ensure reliable ingestion from diverse sources to support reporting and insights.

Collaborate with analysts, product teams, and developers to translate data requirements into technical specifications and end-to-end solutions, while upholding data quality and

Qualifications

• 3+ years in Data Warehouse development.
• Experience analyzing data from multiple sources in cloud environments.
• 2+ years building scalable data stores for business analysis.
• SQL and Python for data retrieval, parsing, and processing.
• ETL/ELT workflows for reporting and analytics in the cloud.
• Experience with Cloudera or Hadoop technologies.
• Ability to obtain a Public Trust or Suitability determination.
• Bachelor's degree and US citizenship or permanent residency.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines for diverse data sources.
• Build data storage solutions (data warehouses, data lakes, or lake houses).
• Perform backend SQL/NoSQL testing and data validation.
• Collaborate with analysts, engineers, and stakeholders to translate requirements into data models and pipelines.
• Develop automation to convert diverse data types into usable formats and monitor cloud platforms.

Skills

SQL

Python

ETL/ELT

Cloud data pipelines

Data warehousing

NoSQL

Spark/Databricks

AI coding tools

Education

Bachelor's degree

Tools

AWS Glue

AWS Athena

Postgres

Redshift

Snowflake

Hadoop

Spark

Databricks

Hive

Kafka

Job description

Metas Solutions is seeking a Data Engineer to design, build, and maintain cloud-based data pipelines and stores to enable analytics across large datasets. You will craft scalable ETL/ELT workflows, structure data storage architectures, and ensure reliable ingestion from diverse sources to support reporting and insights.

Collaborate with analysts, product teams, and developers to translate data requirements into technical specifications and end-to-end solutions, while upholding data quality and