Lead AI-Driven Development: Architect and implement full-stack solutions that integrate AI/ML models and prompt engineering techniques to enhance product functionality and accelerate time-to-market.
GCP Mastery: Design, develop, and deploy scalable applications on Google Cloud Platform, utilizing services like Compute Engine, Cloud Functions, Cloud SQL, and Vertex AI.
Startup Agility: Drive rapid prototyping and MVP development, ensuring alignment with agile methodologies and a focus on iterative improvement.
Full-Stack Leadership: Lead a team of developers, providing technical guidance, mentoring, and fostering a collaborative environment.
Technical Architecture & Strategy: Define and implement robust architectural patterns, ensuring code quality, performance, and maintainability.
Requirements
Bachelor's degree in Computer Science or a related field.
The pay range for this position is $125000.00 - $190000.00/yr.
Responsibilities
Lead and own pre-sales activities, including technical discovery, solution design, proposal development, and client presentations.
Partner closely with Sales and Contract teams to draft highly accurate Statements of Work (SOWs) that translate proposed technical architecture into solid, risk-mitigated delivery contracts.
Conduct technical assessments, Proofs-of-Concept (POCs), and feasibility studies to validate proposed AI and data solutions.
Identify and mitigate technical/operational risks. Prioritize and advocate for critical non-functional requirements (NFRs) including security, scalability, performance, reliability, and supportability.
Support the continued evolution of TGS service offerings. Package repeatable solution patterns, create pre-sales collateral (such as case studies and client decks), and provide feedback to the practice team.
Requirements
Technical Bachelor's degree (Computer Science, Information Technology, Systems Engineering, or equivalent).
Nice to have: Google Cloud Professional Data Engineer certification
Nice to have: Google Cloud Professional Cloud Architect certification
Build and operate connectivity, profiling, and graph/search foundation for enterprise ontology and knowledge management platforms.
Compensation
The annual salary for this position is anticipated to be between $100,000 and $120,000 USD, depending on experience, qualifications, geographic location, and other factors permitted by law.
Responsibilities
Transform raw operational and master data sources into clean, lineage-tracked, governed, and queryable knowledge assets
Design and support data connectivity, metadata management, graph platforms, ingestion frameworks, and search capabilities
Build adapters and integration frameworks for BigQuery, Cloud SQL, REST APIs, Apigee, GCS, Pub/Sub
Establish scalable ingestion patterns for operational, transactional, and master data systems
Support API-based integrations across enterprise applications and data platforms
Requirements
Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related discipline
Build the connectivity, profiling, and graph/search backbone that the ontology runs on — turning raw source systems into clean, lineage-tracked, query able knowledge.
Compensation
The annual salary for this position is between $80,000 - $140,000 depending on experience and other qualifications of the successful candidate.
Responsibilities
Own data source connectivity: build and maintain adapters to connect master data and work-ticket APIs via BigQuery, Cloud SQL, REST/Apigee, GCS, and Pub/Sub.
Own data profiling and inference: profile source data for quality, statistical patterns, dependencies, and lineage ahead of ontology mapping.
Stand up and operate the Knowledge Core — the graph and search backbone — using Spanner Graph (or an equivalent property-graph/RDF platform), Dataplex, BigQuery, and Vector Search.
Build batch and streaming ingestion pipelines that keep the graph and catalog current as source systems change.
Implement data quality checks, schema discovery, and the metadata catalog, surfacing issues to the Architect and Data Domain SME.
Lead the architecture of our modern data stack on BigQuery and dbt, setting the patterns, standards and trade-offs that new and migrated workloads follow.
Responsibilities
Lead the architecture of our modern data stack on BigQuery and dbt
Design and evolve the architecture of our GCP data platform
Own the data ingestion layer
Define and maintain the cross-cutting foundations of the platform
Act as the technical reference for data engineers and analysts
Assurer l’exploitation, la fiabilité et l’évolution de l’infrastructure de la Data Platform (Cloud GCP, clusters Kubernetes, Kafka) en intégrant l’usage de l’IA dans les pratiques d’exploitation.
Responsibilities
Exploitation et administration des environnements Cloud (GCP : GKE, Cloud Storage, Cloud SQL, Cloud Monitoring)
Administration, déploiement et gestion de clusters Kubernetes et des applications associées
Administration d’Apache Kafka, de la configuration avancée à l’exploitation en production (performance, sécurité, supervision)
Mise en œuvre des pratiques DevOps (CI/CD), des bonnes pratiques de sécurité et de gestion des accès
Analyse des impacts techniques et amélioration continue des solutions existantes
Requirements
Bac+5 en école d'ingénieur ou équivalent universitaire avec une spécialisation en informatique
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