Data Engineer and Integrations Lead ( Azure, SQL, English )

MiTek is looking for a Data Engineer & Integrations Lead to design, build, and own key parts of our enterprise data platform. This role combines hands‐on data engineering with architectural ownership, focusing on data models, reliable pipelines, and system integrations across the business.
You will be responsible for how data is ingested, modeled, deployed, and monitored, while collaborating with others in a global team.
Join MiTek's global data platform team to design and build scalable data pipelines and modern data architecture on Azure. You will work with large datasets, modern ETL frameworks, and collaborate with international teams to deliver impactful data solutions.
Key Responsibilities
Data Engineering & Modeling
• Design, implement, and own dimensional data models to support analytics, reporting, and downstream use cases.
• Review and validate data models to ensure consistency, scalability, and alignment with business requirements.
• Define and document data modeling standards, best practices, and guidelines for the organization.
• Continuously assess existing data models and propose improvements as business needs evolve
Data Integration & Pipelines
• Lead the design, development, and maintenance of data pipelines, primarily using Azure Data Factory (ADF) and related Azure data services.
• Build robust ingestion processes from a variety of sources, including databases, files, and third‐party REST APIs.
• Ensure pipelines are reliable, efficient, scalable, and cost‐effective.
• Work closely with other engineers to refactor and optimize existing pipelines and SQL transformations.
CI/CD, Automation & DevOps
• Implement and champion CI/CD practices for data pipelines, SQL scripts, and analytics assets.
• Use Git‐based version control to ensure reproducibility, auditability, and quality.
• Partner with DevOps and IT teams to improve deployment workflows and automation across environments.
Data Operations, Monitoring & Reliability
• Monitor end‐to‐end data flows, from raw ingestion through curated warehouse layers.
• Define data quality checks and ensure facts and dimensions are populated accurately and on time.
• Implement logging, alerting, and monitoring to proactively detect and resolve failures.
• Support root‐cause analysis and implement long‐term fixes for data‐related issues.