Principal Azure Data Platform Engineer

Careington International Corp.; Frisco (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Careington International Corp. is seeking a Principal Data Engineer in Frisco, TX to design and scale enterprise data pipelines and platforms. You will build Azure-based data lake infrastructure and collaborate with analytics, reporting, and product teams to enable dashboards, AI, and ML initiatives.

The role emphasizes data governance, robust ingestion patterns, and leadership for a data engineering team, with a focus on reliability and scalable architecture.

Qualifications

• Bachelor’s degree in a computer science or data-related field.
• 10+ years of data engineering or integration experience.
• Experience building ETL/ELT pipelines with SSIS, Azure Data Factory or similar.
• Python for data processing and orchestration.
• Strong SQL Server and relational DB skills.
• Experience with cloud data platforms in Azure.
• Knowledge of data lake patterns (Bronze, Silver, Gold).

Responsibilities

• Design, build, and optimize scalable data pipelines and platforms.
• Develop Azure-based data lake architecture using medallion patterns.
• Ensure data quality, governance, and lineage across datasets.
• Collaborate with analytics, reporting, and application teams.
• Mentor junior data engineers and provide technical guidance.
• Support dashboards, AI/ML pipelines, and enterprise data initiatives.
• Monitor performance and optimize ingestion of client data and claims.

Skills

ETL pipelines

Azure Data Factory

SSIS

Python

SQL Server

Data governance

Data validation

Data lake

Communication

Education

Bachelor's degree in Computer Science / IT / Data Engineering

Tools

Power BI

Job description

Careington International Corp. is seeking a Principal Data Engineer in Frisco, TX to design and scale enterprise data pipelines and platforms. You will build Azure-based data lake infrastructure and collaborate with analytics, reporting, and product teams to enable dashboards, AI, and ML initiatives.

The role emphasizes data governance, robust ingestion patterns, and leadership for a data engineering team, with a focus on reliability and scalable architecture.