Why This Role Exists

Scope & Scale

• Large-scale multi-tenant SaaS environment supporting thousands of enterprise customers
• High-volume transactional and operational data workloads
• Complex third-party integrations spanning multiple data formats and protocols
• Real-time streaming systems and event-driven architectures
• Multiple optimization and intelligent automation initiatives requiring scalable feature engineering and telemetry systems

What You Will Own

Data Platform Architecture

• Design and own the end-to-end data platform architecture including raw ingestion, canonical entity modeling, analytics layers, feature infrastructure, and telemetry systems.
• Build scalable frameworks for feature engineering, data quality, lineage, governance, and operational reliability.
• Define patterns for tenant isolation, auditability, retention policies, and secure data access at scale.

Feature Store & AI Data Infrastructure

• Lead the design and operation of online and offline feature infrastructure supporting real-time scoring and model training workflows.
• Establish standards for feature ownership, freshness SLAs, training/inference parity, metadata governance, and lifecycle management.
• Build scalable pipelines supporting ML models, intelligent automation workflows, and AI-enabled platform capabilities.

Streaming & Event-Driven Systems

• Design and operate real-time streaming pipelines, event-driven data propagation, and change-data-capture systems.
• Support low-latency feature materialization and telemetry processing across distributed services.
• Ensure scalability, observability, and reliability across streaming infrastructure.

Data Governance & Quality

• Establish enterprise-grade governance frameworks covering PII handling, tenant isolation, retention policies, audit logging, and cost attribution.
• Implement automated monitoring for schema drift, stale pipelines, null spikes, feature skew, and data inconsistencies.
• Own data quality standards and operational accountability across the platform.

Platform Modernization & Migration Strategy

• Lead data migration and modernization initiatives across relational and NoSQL systems.
• Define scalable migration patterns including dual-write validation, reconciliation strategies, and zero-downtime cutovers.
• Partner with Engineering, Infrastructure, Product, and AI teams to align platform evolution with business goals.

Technical Leadership

• Mentor engineers and data practitioners across multiple teams.
• Drive architecture reviews, data modeling standards, and platform engineering best practices.
• Establish scalable operating mechanisms and technical governance processes.

Technical Environment

Data Platforms & Storage

• Relational and NoSQL systems including Aurora/MySQL/PostgreSQL, DynamoDB, S3 data lakes, and distributed storage architectures
• Modern lakehouse and analytical storage patterns

Streaming & Event Systems

• Event-driven architectures, streaming pipelines, CDC frameworks, and real-time feature propagation systems

AI & ML Infrastructure

• Feature stores, model training pipelines, model lifecycle infrastructure, and AI-enabled workflow systems
• Production AI/ML deployment and observability frameworks

Data Engineering & Analytics

• ETL/ELT orchestration platforms
• Distributed analytics environments and BI tooling
• Metadata cataloging, governance, and observability systems

Infrastructure & DevOps

• AWS cloud infrastructure, infrastructure-as-code, CI/CD, monitoring, and operational tooling

Hands-On Expectations

• hands-on engineering and SQL/data work
• architecture and systems design
• technical mentorship and cross-functional leadership

First 12 Months

Months 1-3

• Evaluate the existing data landscape, integrations, pipelines, and governance gaps
• Define canonical entity models and future-state data architecture standards
• Publish initial architecture recommendations and modernization priorities

Months 4-6

• Establish foundational feature infrastructure and governance frameworks
• Deliver initial modernization and migration initiatives
• Implement automated monitoring and data quality controls

Months 7-9

• Support production deployment of optimization and intelligent automation workflows
• Operationalize feature lifecycle management, telemetry systems, and model infrastructure
• Expand streaming and event-driven capabilities across the platform

Months 10-12

• Deliver additional production-scale optimization and AI-enabled data capabilities
• Mature governance, observability, and platform reliability standards
• Define long-term roadmap for data platform scalability and intelligent systems evolution

Requirements

Required Qualifications

• 7+ years of experience in data engineering, platform engineering, or data architecture roles
• Experience operating at a senior architect, principal, or platform leadership level
• Deep expertise in both relational and NoSQL data modeling and distributed data systems
• Strong experience building scalable feature infrastructure and real-time data platforms
• Hands-on experience with cloud-native AWS data and analytics services
• Experience supporting AI/ML workflows, feature engineering, or production AI systems
• Strong understanding of event-driven architectures, CDC, and streaming data systems
• Experience with large-scale multi-tenant platform architectures and governance challenges
• Strong data governance instincts around security, auditability, PII handling, and operational accountability
• Ability to balance architecture leadership with hands-on implementation work

Strongly Preferred

• Experience supporting AI/ML lifecycle infrastructure including feature stores, model registries, monitoring, and production deployment workflows
• Experience with LLM-related infrastructure, embeddings pipelines, retrieval systems, or intelligent automation workflows
• Experience building closed-loop analytics or optimization systems with telemetry and outcome tracking
• Strong experience with data quality monitoring and anomaly detection at scale
• Familiarity with lakehouse architectures, open table formats, and large-scale analytical systems

Nice to Have

• Experience in SaaS, fintech, automotive, marketplace, or transaction-heavy industries
• Familiarity with complex third-party integrations and heterogeneous data sources
• Experience with vector databases or semantic retrieval systems
• Exposure to optimization, recommendation, scheduling, or operational intelligence systems

Benefits & Culture

• Comprehensive medical, dental, and vision coverage
• Employer-sponsored disability and life insurance
• 401(k) with company match
• Generous paid time off and company holidays
• High-impact ownership over foundational AI and data platform initiatives
• Opportunity to shape next-generation intelligent systems at scale