Data Engineer

The Data Engineer is responsible for the development, administration, and continuous improvement of Krayden's enterprise data integration and analytics platform. This role designs, builds, supports, and optimizes enterprise data pipelines, data integration processes, and data platform services that enable reporting, analytics, business intelligence, and AI initiatives across the organization.

The position serves as a key contributor to the implementation and long-term support of Krayden's Enterprise Data Platform while partnering with implementation providers to develop internal expertise, establish repeatable integration patterns, and support the onboarding of newly acquired businesses. The role collaborates closely with Data Operations, Business Intelligence, ERP, and business stakeholders to ensure reliable, scalable, and well-governed enterprise data solutions.

Overall Responsibilities

• Develop, maintain, and continuously improve enterprise data integration, pipeline, and data platform solutions.
• Support enterprise data architecture, data ingestion, transformation, storage, and integration processes.
• Design and maintain reusable data integration patterns that improve scalability, consistency, and operational efficiency.
• Support enterprise reporting, analytics, AI, and business intelligence initiatives through reliable and high-quality data services.
• Collaborate with internal teams and implementation partners to support platform enhancements, acquisitions, and enterprise technology initiatives.
• Contribute to data governance, documentation, automation, operational monitoring, and continuous improvement activities.

Core Responsibilities

• Design, develop, monitor, and support enterprise data pipelines, data integration workflows, and data movement processes.
• Support enterprise data platforms, data warehouses, data lakes, semantic models, and reporting environments.
• Monitor data quality, pipeline performance, scheduled processing, and data availability while resolving integration and operational issues.
• Develop reusable integration patterns that support acquisitions, new business systems, and enterprise growth.
• Create and maintain technical documentation, source-to-target mappings, operational procedures, and data standards.
• Support data platform enhancements, production deployments, testing, and operational support activities.
• Partner with Business Intelligence, ERP, Infrastructure, and business teams to support enterprise reporting and analytics solutions.

Desired Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or equivalent professional experience.
• 3-6 years of experience developing and supporting enterprise data integration and analytics platforms.
• Strong understanding of SQL, data modeling, ETL/ELT processes, and enterprise data architecture.
• Experience supporting cloud-based data platforms and enterprise reporting environments.
• Experience troubleshooting data integration, pipeline, and data quality issues.
• Strong analytical, communication, documentation, and problem-solving skills.
• Ability to manage multiple priorities within both operational support and project delivery environments.

Desired Technologies

• Microsoft Fabric (Warehouse/Lakehouse), ADF, Synapse
• Power BI/Apss/Automate, SQL, Python, Spark
• Artificial Intelligence tools and processes
• REST APIs
• Data integration and ETL/ELT frameworks
• Medallion Architecture
• Technical ownership and accountability
• Analytical thinking and problem solving
• Collaboration across technical and business teams
• Effective written and verbal communication
• Documentation and knowledge management
• Adaptability and continuous learning

Business Outcomes

• Reliable, scalable, and well-governed enterprise data integration and analytics platforms.
• Improved data quality, availability, and operational reliability across enterprise reporting solutions.
• Successful knowledge transfer and long-term internal ownership of Krayden's Enterprise Data Platform.
• Repeatable and scalable onboarding of acquisitions and new enterprise data sources.
• Reduced operational risk through standardized pipelines, automation, documentation, and knowledge sharing.
• Strong collaboration between Data Engineering, Business Intelligence, ERP, and business stakeholders to support enterprise reporting, analytics, and AI initiatives.