Head of Data Platform

KORE1, a nationwide provider of staffing and recruiting solutions, has an immediate opening for a Head of Data Platform that is fully remote.


Why This Role Exists

We operate a large-scale, multi-tenant SaaS platform supporting thousands of enterprise customers nationwide. Our data ecosystem - spanning relational databases, NoSQL systems, streaming events, and cloud-based data lakes - powers complex transactional workflows, integrations, analytics, and operational decision-making at scale.

As the platform evolves, we are building intelligent, closed-loop optimization systems capable of observing operational signals, predicting outcomes, automating decisions, and continuously learning from results. These systems rely on scalable feature engineering, real-time data pipelines, model infrastructure, telemetry, and strong governance controls.

This role will own the foundational data architecture that enables those capabilities. You will lead the design of the data platform, feature infrastructure, governance framework, and real-time data systems that transform a transactional SaaS platform into an intelligent decision engine.

This is a highly hands-on leadership role focused on modern data architecture, AI/ML infrastructure, streaming systems, governance, and scalable platform engineering.
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

This is not a purely strategic leadership role. The expectation is a strong balance of:

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

You should be comfortable operating both as a high-level architect and as a deeply technical contributor on critical systems and pipelines.
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

We are a fast-paced and collaborative technology organization focused on building scalable platforms that improve operational efficiency, intelligent automation, and customer outcomes.

We offer:

• 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

Job Requirements

Compensation depends on experience but is typically 200-250k

ABOUT KORE1
Specializing in professional and technical recruiting, KORE1 is committed to supporting top IT, Engineering, Creative, Scientific, Accounting and Finance professionals in their career paths. We build deep relationships with leading companies, connecting them to exceptional talent every day. With extensive industry expertise and unmatched opportunities, our goal is to provide a unique experience for our contractors and consultants as they prepare for their next role. We are passionate about matching the right people with the right companies.

Kore1 provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Kore1 complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Kore1 expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of Kore1's employees to perform their job duties may result in discipline up to and including discharge.