Senior Data Engineer (AI/ML)

Location: On-site in Kansas City, Kansas, United States; travel required 15–25% (1–2 times per quarter).

Salary: $99,000–$139,000 per year.

Company

The organization develops enterprise AI/ML solutions supported by secure data infrastructure and AI-enabled applications.

What you will do

• Design, build, and optimize scalable ETL/ELT pipelines for structured and unstructured enterprise data used in AI/ML training and inference.
• Improve data accuracy, reliability, security, and freshness through cleansing, validation, automation, monitoring, error handling, and quality metrics.
• Troubleshoot complex integration and performance issues using caching, distributed processing, and specialized ETL patterns.
• Own AI data infrastructure components, including monitoring, logging, documentation, data lineage, and maintainability.
• Apply enterprise architecture, AI governance, privacy, security, coding, and CI/CD standards while communicating risks to leadership.
• Develop and support accessible single-page AI applications using TypeScript, JavaScript, Vue, and Quasar.

Requirements

• Undergraduate degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 8+ years of experience in data engineering, software development, or a related discipline, including AI/ML data pipelines.
• Proficiency with Python, SQL, PySpark, Snowflake, Databricks, and Apache Airflow.
• Experience with relational and NoSQL databases, data warehouses or lakes, Azure Data Factory, Kafka, and a major cloud data ecosystem, preferably Azure.
• Experience with TypeScript or JavaScript and modern single-page applications; Vue and Quasar experience is preferred.
• Must be able to read, write, and speak English and work on-site in Kansas City, Kansas.

Nice to have

• Cloud data engineering or data privacy/security certifications.
• Experience with MLOps or AI/ML lifecycle management.
• Familiarity with infrastructure-as-code and automated deployment practices.

Culture & Benefits

• Cross-functional collaboration with software engineers, data architects, domain experts, product teams, and platform teams.
• Opportunity to lead technical initiatives, influence cross-team decisions, and advance data engineering standards.
• Regular business travel of 15–25%, typically 1–2 times per quarter.