Senior AI Context Engineer

Jobtailor; Missouri; On-site; USD 150,000 - 190,000; Full time

Job summary

Jobtailor in Missouri seeks an experienced data engineering leader to design enterprise semantic models and KPI layers ensuring trusted metrics across analytics and AI use cases.

You will build scalable batch and real-time pipelines in a Google Cloud environment and collaborate with AI/ML teams to grounding strategies for AI applications and agents.

Qualifications

• 10–15+ years of experience in enterprise-scale cloud data engineering and distributed systems.
• Strong hands-on experience building modern data platforms in GCP (BigQuery, Dataform, Pub/Sub, Composer/Airflow, Cloud Run).
• Deep expertise in SQL, Python, and data modeling (dimensional modeling, lakehouse architectures).
• Experience with open-source tech stack such as Iceberg, Trino, Kubernetes, Docker etc.
• Hands-on experience with metadata management, lineage tracking, observability, and data quality frameworks.
• Experience building both batch and real-time streaming data systems.
• Strong understanding of semantic modeling, business metric governance, and AI consumption patterns.

Responsibilities

• Design and implement enterprise semantic models and certified KPI layers to ensure trusted, reusable business metrics.
• Build AI-safe data abstraction layers that prevent metric recomputation and ensure consistency across analytics and AI use cases.
• Develop and enforce data contracts, metadata standards, and semantic governance frameworks across domains.
• Engineer scalable batch and real-time streaming data pipelines in a modern cloud environment (GCP preferred).
• Collaborate with AI/ML teams to design reliable grounding strategies for AI applications and agents.
• Implement metadata management capabilities including cataloging, lineage, observability, and automated data quality checks.
• Apply DataOps principles including CI/CD, automated testing, and deployment automation for data products.
• Support domain-oriented and microservices-based data architecture patterns.
• Mentor engineers and promote best practices in semantic modeling, governance, and AI-ready platform design.

Skills

Data Modeling

Dimensional Modeling

Lakehouse Architectures

Batch Data Systems

Real-Time Streaming Data Systems

Data Quality Frameworks

Lineage Tracking

Observability

DataOps Principles

Automated Testing

Mentoring

Collaboration

Education

Bachelor's or Master's in Computer Science, Data Engineering, or related field

Tools

BigQuery

Dataform

Pub/Sub

Composer/Airflow

Cloud Run

Iceberg

Trino

Kubernetes

Docker

Job description

Responsibilities

• Design and implement enterprise semantic models and certified KPI layers to ensure trusted, reusable business metrics.
• Build AI-safe data abstraction layers that prevent metric recomputation and ensure consistency across analytics and AI use cases.
• Develop and enforce data contracts, metadata standards, and semantic governance frameworks across domains.
• Engineer scalable batch and real-time streaming data pipelines in a modern cloud environment (GCP preferred).
• Collaborate with AI/ML teams to design reliable grounding strategies for AI applications and agents.
• Implement metadata management capabilities including cataloging, lineage, observability, and automated data quality checks.
• Apply DataOps principles including CI/CD, automated testing, and deployment automation for data products.
• Support domain-oriented and microservices-based data architecture patterns.
• Mentor engineers and promote best practices in semantic modeling, governance, and AI‑ready platform design.

Requirements

• 10–15+ years of experience in enterprise‑scale cloud data engineering and distributed systems.
• Strong hands‑on experience building modern data platforms in GCP (BigQuery, Dataform, Pub/Sub, Composer/Airflow, Cloud Run).
• Deep expertise in SQL, Python, and data modeling (dimensional modeling, lakehouse architectures).
• Experience with open‑source tech stack such as Iceberg, Trino, Kubernetes, Docker etc.
• Hands‑on experience with metadata management, lineage tracking, observability, and data quality frameworks.
• Experience building both batch and real‑time streaming data systems.
• Strong understanding of semantic modeling, business metric governance, and AI consumption patterns.

Hard Skills

• Data Modeling
• Dimensional Modeling
• Lakehouse Architectures
• Batch Data Systems
• Real‑Time Streaming Data Systems
• Data Quality Frameworks
• Lineage Tracking
• Observability
• DataOps Principles
• Automated Testing

Soft Skills

• Mentoring
• Collaboration