Senior AI Context Engineer

KION Group

Semantic Context Data & AI Engineer developing AI-native, context-aware data solutions at Dematic. Requires expertise in cloud data engineering and AI-oriented platform design.

Posted 7/2/2026full-timeAtlanta • Missouri, Wisconsin • 🇺🇸 United StatesSenior💰 $134,250 - $179,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in enterprise-scale cloud data engineering, focusing on building modern data platforms in GCP and implementing robust data governance frameworks. Proficient in developing scalable data pipelines and applying DataOps principles to ensure high-quality data products.

Highest-signal resume keywords

GCP Data Platform DevelopmentSQL ExpertisePython ProgrammingMetadata ManagementSemantic Modeling

ATS Keywords

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Hard Skills

Data ModelingDimensional ModelingLakehouse ArchitecturesBatch Data SystemsReal-Time Streaming Data SystemsData Quality FrameworksLineage TrackingObservabilityDataOps PrinciplesAutomated Testing

Soft Skills

MentoringCollaboration

Tools & Technologies

BigQueryDataformPub/SubComposer/AirflowCloud RunIcebergTrinoKubernetesDocker

Industry Keywords

Enterprise Semantic ModelsKPI LayersData ContractsSemantic GovernanceAI ApplicationsAI Consumption Patterns

Tech Stack

Tools & technologies

AirflowBigQueryCloudDistributed SystemsDockerGoogle Cloud PlatformKubernetesMicroservicesPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

Career Development
Competitive Compensation and Benefits
Pay Transparency
Global Opportunities