Principal Data Engineer

Careington International Corporation•Frisco, TX

6d•Onsite

About The Position

Careington International is hiring a Principal Data Engineer to design, build, and optimize scalable data pipelines and platforms supporting operational processing, analytics, and data science initiatives. The role focuses on enterprise data integration for client eligibility, claims, and provider network datasets, and will help expand the company’s Azure-based data platform, including a data lake built with the medallion architecture (Bronze, Silver, Gold). The engineer will partner with analytics, reporting, and application development teams to deliver reliable data, improve data quality, and provide scalable infrastructure for dashboards, artificial intelligence, and machine learning initiatives. The position is based at Careington’s corporate location on Gaylord Parkway in Frisco, Texas. It also provides technical guidance and mentorship to junior engineers and developers and collaborates with database administrators, application developers, infrastructure teams, business stakeholders, and other cross-functional partners.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field.
10 or more years of experience in data engineering, data integration, or related roles.
Strong experience building ETL/ELT pipelines using SSIS, Azure Data Factory, or similar integration platforms.
Experience with Python for data processing, automation, or pipeline orchestration.
Strong experience with SQL Server and relational databases.
Experience designing scalable data ingestion and transformation processes for large datasets.
Experience building or supporting cloud-based data platforms within Microsoft Azure.
Strong problem-solving and analytical thinking abilities.
Ability to design scalable, maintainable, and reusable data integration frameworks.
Strong communication skills, including explaining complex technical concepts to technical and non-technical stakeholders.
Experience collaborating with cross-functional teams, including data analysts, data scientists, application developers, and infrastructure engineers.
Ability to manage multiple priorities in a fast-paced environment.
Strong attention to detail and commitment to data quality and operational reliability.
Ability to mentor and provide technical leadership within a data engineering team.

Nice To Haves

Experience designing and implementing data lake architectures.
Experience with medallion data architecture (Bronze, Silver, Gold).
Experience supporting analytics platforms such as Power BI or similar BI tools.
Experience with distributed data processing or big data technologies.
Experience supporting AI/ML data pipelines and analytics workloads.
Experience with healthcare, insurance, or large-scale transactional data environments.

Responsibilities

Design, develop, and maintain scalable data pipelines using SSIS, Azure Data Factory, and Python.
Optimize inbound data ingestion frameworks for client eligibility, claims, and provider network data from external partners.
Develop reusable patterns for secure, efficient ingestion of structured and semi-structured data from file transfers, APIs, and other integration methods.
Design and implement data validation, monitoring, and logging frameworks to support data integrity and operational visibility.
Build and maintain an Azure data lake using the Bronze, Silver, and Gold medallion design pattern.
Develop transformation processes to standardize, cleanse, and prepare enterprise datasets for reporting, analytics, and downstream applications.
Collaborate with analytics and reporting teams to deliver curated datasets for dashboards, AI, and machine learning models.
Design scalable inbound and outbound data integration frameworks for client onboarding and enterprise data exchange.
Monitor and tune pipeline performance for efficient data movement, minimal latency, and reliable execution.
Implement data governance, metadata management, and data lineage practices.
Participate in architectural discussions and recommend improvements to data infrastructure, tools, and processes.
Provide mentorship and technical guidance to junior data engineers and developers.
Collaborate with database administrators, application developers, infrastructure teams, business stakeholders, and other cross-functional teams on enterprise data initiatives.