Why This Job is Featured on The SaaS Jobs
This Data Architect position sits at a common inflection point in modern SaaS: unifying data across multiple products and customer environments into a single platform that can support analytics, APIs, and AI use cases. The remit spans hybrid realities that many SaaS businesses face, where on prem systems and cloud services must coexist, and where multi tenancy and client isolation are core architectural constraints rather than afterthoughts.
For a SaaS career, the role builds durable platform thinking that transfers across product led and enterprise SaaS contexts. Owning data models, governance, and ingestion patterns creates fluency in how SaaS companies turn operational data into usable, secure capabilities for internal teams and external customers. The emphasis on self service analytics and customer facing endpoints also develops an understanding of data as a product surface, not only a back office function.
This role tends to suit architects who prefer defining standards and reference designs, then guiding delivery through influence and documentation. It also aligns with professionals motivated by cross functional problem solving, particularly where product, AI, compliance, and cloud architecture intersect around shared data foundations.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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.
