Principal Data & AI Architect
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Job Description
Principal Data & AI Architect designing secure agent runtimes, APIs, and scalable pipelines for Publicis Groupe’s marketing technology products. Mentoring engineers and setting cross-platform architecture.
Responsibilities:
- Set the technical direction for OneSuite data products and their integration into the Performics platform
- Write code and design cross-repository architecture for reliable, scalable systems
- Lead high-stakes technical decisions and establish engineering standards
- Build backend and data foundations for AI-assisted analyst workflows using agent and tool runtimes
- Own architecture across marketing data pipelines, connector systems, platform APIs, and agent/tool runtimes
- Define domain boundaries and data contracts between warehouse outputs, APIs, agent tools, and user-facing workflows
- Create incremental technical plans balancing reliability, security, tenant isolation, developer experience, and maintainability
- Evolve medallion-architecture data pipelines, including data quality, schema enforcement, lineage, and curated outputs
- Harden ingestion from external marketing platforms and APIs
- Architect backend services, cloud infrastructure, stable contracts, streaming workflows, and tool permissions
- Design agent and tool-runtime systems with secure multi-tenant access and durable boundaries
- Implement authentication, authorization, rate limiting, and multi-tenant access controls
- Integrate external APIs, cloud services, internal microservices, and agent/tool runtimes
- Build and maintain deployment pipelines and infrastructure environments
- Implement tracing, structured logging, metrics, and alerting
- Mentor engineers, contribute to internal libraries and backend frameworks, and drive platform consistency
Requirements:
- At least ten years of experience in backend, data platform, or distributed systems
- At least two years of hands-on production experience building with LLMs and agent frameworks
- Experience integrating Claude Agent SDK, MCP-style tooling, or similar agent/tool runtimes into live systems
- Experience architecting agent and tool-runtime systems with tool permissioning and secure multi-tenant access
- Expertise in Python, API design, service boundaries, async and concurrent systems, and modern testing
- Proficiency with Databricks, PySpark, Lakehouse patterns, and medallion-style data architecture, or equivalent large-scale data pipeline experience
- Strong AWS serverless experience with Lambda, API Gateway, S3, IAM, and CloudFormation/SAM or equivalent infrastructure-as-code tooling
- Deep understanding of multi-tenant SaaS design, authentication and authorization, tenant-scoped data access, secrets management, and operational security
- Experience designing stable data contracts, schema evolution patterns, and consumer-facing data models
- Proficiency with SQL and NoSQL databases such as PostgreSQL, Redis, or MongoDB
- Strong CI/CD experience and knowledge of observability practices including logs, metrics, and traces
- Technical leadership through mentoring, architecture decisions, code reviews, communication, and engineering standards
- Fluent in English, both verbal and written
- Familiarity with Fivetran, custom connector development, or third-party data ingestion operations is advantageous
- Knowledge of Delta Lake, Unity Catalog, data governance, data lineage, Lakehouse performance optimization, streaming or queuing systems, and marketing or advertising APIs would be beneficial
Benefits:
- Diversity and inclusion commitment
- Continuous learning and knowledge sharing
- Mentoring and professional growth opportunities
- Remote work arrangement
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