Overview

We’re seeking a highly skilled Data Architect to design and lead the evolution of Advantive’s unified data platform. This role defines the architectural foundation that enables product teams, customers, and AI services to access consistent, secure, and high-quality data across the Advantive ecosystem.

The Data Architect will design data pipelines, integration frameworks, and governance models that connect on-prem and SaaS products into a centralized data lake and warehouse. In collaboration with our architecture team, this role will be instrumental in enabling self-service analytics, customer-facing APIs, and AI-powered insights.

If you’re passionate about data architecture, API-driven design, and building enterprise-scale analytics platforms, this is an opportunity to define how Advantive’s customers unlock the value of their operational data.

Responsibilities

Architecture and Design

• Lead the design of the data platform — a unified data platform integrating data from multiple Advantive products and customer environments.
• Architect data ingestion, transformation, and delivery pipelines using cloud-native services
• Define and maintain the enterprise data model supporting analytics, APIs, and AI use cases.
• Develop standards for data quality, lineage, versioning, and change tracking
• Design the data warehouse schema, optimized for performance, scalability, and multi-tenancy.
• Implement APIs and query endpoints to expose clean, secure datasets for customers and internal AI agents.
• Partner with the Cloud Architect on hybrid data exchange — ensuring on-prem connectors, synchronization, and resilience during outages.

Integration and Delivery

• Define data ingestion patterns including direct database connections, API-based integration, and flat-file ingestion.
• Manage the configuration and orchestration of data pipelines and Power BI datasets.
• Design data lake storage strategies to support efficient querying and cost-effective storage.
• Establish data mapping, transformation, and validation processes for newly acquired products joining the platform.
• Enable real-time or near-real-time data streaming where appropriate for operational insights.

Data Governance and Access

• Define and enforce data governance standards across all platform components.
• Implement role-based and row-level security (RLS) to ensure client isolation and compliance with data-sharing agreements.
• Collaborate with legal and compliance teams to enforce regional data privacy, retention, and restricted-client rules.
• Own the metadata catalog, data dictionary, and business glossary.
• Partner with analytics and AI teams to ensure consistent and compliant access to data for model training and inference.

Collaboration and Leadership

• Work closely with Product, AI, and Architecture teams to ensure alignment on shared data models and services.
• Provide technical leadership to developers and data engineers implementing data pipelines and integrations.
• Communicate architecture decisions through documentation, diagrams, and presentations to technical and non-technical audiences.
• Mentor engineers and promote data literacy across the organization.