Data Platform Architect / Lead Data Engineer at DataArt – London | Jobyssey

The Data Platform Architect is the premier technical authority within Data Engineering. Serving as a senior individual contributor, this role owns the end-to-end technical strategy and system architecture of the data platform. Operating with a high degree of autonomy, the Data Platform Architect partners closely with EPD (Engineering, Product, Design) leadership to make critical high-stakes architectural decisions, establish company-wide data patterns, and ensure platform alignment with long-term business goals.

Own the architectural blueprint and long-term technical vision for the global data platform, sequencing delivery incrementally to avoid high-risk migrations.
Architect the analytics, semantic, and AI data layers to securely expose trustworthy metrics, feature usage signals, and skill intelligence models across the enterprise.
Address and resolve high-complexity architectural challenges surrounding multi-tenant isolation, consistency, streaming/batch processing performance, and data contracts.
Formulate architectural standards, governance frameworks, and data modeling conventions adopted across engineering teams.
Oversee platform-level observability, data quality frameworks, SLAs, and lead root cause analysis (RCA) on systemic platform failures.
Guide and mentor senior engineering staff on system design, technical trade-offs, and architectural decision-making.

Proven experience in a Data Platform Architect or Staff-level Data Engineering role designing and scaling enterprise data platforms.
Deep expertise in data architecture, data warehousing, data lakes, and both real-time (streaming/CDC) and batch processing paradigms.
Strong hands-on proficiency in Python for back-end engineering and platform-level software design.
Demonstrated expertise in modern data transformation frameworks, specifically complex dbt project architecture.
Extensive background in SQL and NoSQL database schema design, modeling at scale, and multi-tenant isolation patterns.
Experience with Infrastructure as Code (IaC) and containerization frameworks (e.g., Terraform, Kubernetes) to support platform infrastructure.
Expertise in defining data quality, observability, data contract, and incident management standards.
Exceptional executive-level communication and stakeholder management skills with a proven ability to articulate architectural trade-offs.

Hands-on experience with TypeScript / Node.js back-end environments.
Practical familiarity with BigQuery, PostgreSQL, MongoDB, and Apache Kafka.
Experience integrating customer event collection platforms (e.g., Segment) into unified data architectures.
Direct experience designing data modeling architectures for AI, ML, or agentic workloads.

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