Data Architect

AgencyBloc is a leading provider of agency management solutions built specifically for life and health insurance agencies. Our platform helps agencies streamline operations, strengthen client relationships, and drive growth through powerful CRM, commission processing, and marketing automation tools. We value innovation, collaboration, and making a meaningful impact in the industries we serve and we take pride in fostering a culture that genuinely cares about our customers, our work, and each other. **Summary:** As a Data Architect, you will define and evolve AgencyBloc's data architecture and analytics platform strategy, establishing the vision, standards, and patterns that enable secure, scalable, and reliable data products across our SaaS platforms. You will lead the design of our cloud data platform, defining how data is ingested, modeled, governed, and served using a modern lakehouse approach. This role is responsible for translating business and engineering needs into long-term data strategies and ensuring those strategies are adopted effectively across teams. You will partner closely with engineering, analytics, product, and security leadership to guide technical direction, evaluate tools and platforms, and drive modernization of how AgencyBloc captures and delivers value from its data. **Responsibilities:** - Define and own the data architecture strategy across all environments, platforms, and data domains. - Establish and maintain architectural standards, patterns, and best practices for data ingestion, storage, modeling, and consumption. - Design scalable, secure, and resilient data architectures on AWS, including data lake, lakehouse, warehouse, and serving layers. - Define and champion a Medallion (bronze/silver/gold) architecture, establishing standards for raw, refined, and curated data layers and the promotion patterns between them. - Lead the platform direction of AgencyBloc's cloud data warehouse/lakehouse, defining how each is used, where workloads run, and how cost and performance are managed. - Establish data modeling standards (dimensional, Data Vault, and other patterns) that balance flexibility, performance, and maintainability. - Define standards for batch and streaming pipelines, ETL/ELT frameworks, orchestration, and reuse patterns across teams. - Lead tool and platform selection decisions (warehouse/lakehouse, ingestion, transformation, orchestration, catalog, BI), ensuring alignment with long-term strategy. - Conduct architecture reviews and provide guidance on complex or high-impact data initiatives. - Collaborate with engineering leadership to align data capabilities with product and business priorities. **Data Governance, Quality & Compliance** - Define and own the data governance strategy, including data cataloging, lineage, classification, and ownership models. - Establish data quality standards and frameworks, including validation, monitoring, and remediation practices. - Partner with security and compliance teams to enforce secure-by-design principles for sensitive and regulated data, including access controls, encryption, masking, and PII handling (SOC 2 and relevant standards). - Define data retention, archival, and lifecycle management standards. **Observability & Operational Excellence** - Partner with the DevOps Architect to define data pipeline observability standards, including monitoring, alerting, and SLAs/SLOs for freshness, completeness, and reliability, ensuring alerts surface high-quality, actionable signals tied to business impact with minimal noise. - Establish practices for cost monitoring and optimization across data platforms. **Platform Standardization & Leadership** - Drive standardization and reduction of tool and pattern fragmentation across data teams. - Mentor data engineers and senior data engineers, providing technical leadership and architectural guidance. **AI & Future Data Strategy** - Define the organization's approach to enabling AI and ML workloads on the data…