Data Platform Architect / Lead Data Engineer

Client: A leading global Talent Operating System and AI-driven HR technology provider. The company serves enterprise clients—including Fortune 2000 companies—by utilizing advanced skills, task intelligence, and machine learning models to optimize workforce and talent management.

Position overview: 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.

• Responsibilities: 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.

• Requirements: 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.

• Nice to have: 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.