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 platform, including feature data, governance, and adoption strategy. Establish patterns for delivering trusted, well‑governed data to AI/ML and analytics use cases. Evaluate emerging data and AI capabilities and incorporate them into the platform roadmap where they provide measurable value. Skills / Education / Experience Bachelor’s degree in Computer Science or equivalent experience preferred. 10+ years of experience in data engineering, data architecture, or analytics engineering. Proven experience designing and implementing large‑scale cloud data architectures (AWS preferred). Hands‑on expertise with Databricks and/or Snowflake in a production environment. Demonstrated experience designing and implementing Medallion (bronze/silver/gold) architectures. Strong experience with data modeling (dimensional, Data Vault, and related patterns) for analytical and operational use cases. Deep experience with ETL/ELT design, orchestration, and batch and streaming data pipelines at scale. Strong understanding of data governance, cataloging, lineage, and data quality frameworks. Expertise in data security and compliance, including access management, encryption, and PII handling (SOC 2 and similar frameworks). Strong background in SQL, automation, scripting, and modern data engineering practices (e.g., IaC and CI/CD for data). Experience evaluating and selecting data tools and platforms. Experience leading cross‑team technical initiatives and influencing engineering direction. Strong communication skills, with the ability to translate complex technical concepts to non‑technical stakeholders. Experience working in insurance, InsurTech, or other regulated industries is a plus. Strategic thinking and long‑term planning. Systems design and architectural decision‑making. Cross‑team influence and alignment. Balancing standardization with team autonomy. Pragmatic execution and decision‑making. Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States. #J-18808-Ljbffr AgencyBloc