Role: Jr Data Engineer
Location: Frisco, TX
Must Have Skills –
Skill 1 – Yrs of Exp –Data Engineering
Skill 2 – Yrs of Exp –Data Bricks
Skill 3 – Yrs of Exp –Python
"Must be legally authorized to work in the United States without employer sponsorship."
Please ensure that candidates have graduated from the following colleges only with GPA above 3.5
Role Summary
The Data Engineer is a hands-on builder who executes against decomposed deliverables across the same end-to-end vertical owned by Senior Data Engineers on the team — from ingestion, through modeling and transformation, into the Microsoft Fabric semantic layer and Power BI visualizations. The Data Engineer takes solution designs that Senior Data Engineers have shaped with the business and turns them into working, tested, documented, production-grade code. Light interaction with HR business stakeholders is expected and will grow over time as the engineer matures into the Senior role, but business decomposition and solution design primarily sit with the Senior Data Engineers. The Data Engineer must be located in the United States.
Required Qualifications
Bachelor's degree in Computer Science, Software Engineering, Information Management, or equivalent experience in field — plus 4+ years of related work experience.
Must be located in the United States.
4+ years of hands-on data engineering experience delivering production data pipelines in enterprise environments.
Strong proficiency in SQL and Python, including PySpark and Spark SQL for distributed data transformation.
Hands-on experience with Databricks including Delta Lake, Unity Catalog, and workflow orchestration.
Hands-on experience with Snowflake at production scale.
Working experience with Microsoft Fabric including OneLake; familiarity with Fabric IQ semantic layer concepts.
Working experience building data visualizations and reports in Power BI.
Experience implementing data ingestion pipelines using batch, CDC, API, or streaming patterns within a unified ingestion framework.
Solid data modeling skills, including dimensional modeling and lakehouse modeling patterns at the physical implementation level.
Experience implementing pipeline testing — unit tests, integration tests, data quality checks, and reconciliation.
Experience with DevOps practices for data pipelines — Git, CI/CD, and automated testing.
Good communication skills, with the ability to convey technical progress and ask clarifying questions of both technical leads and business stakeholders.
Strong problem-solving skills and the ability to execute independently on well-defined technical work in a fast-paced, agile environment.