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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Architect - **Company:** RFG Advisory - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $160,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, ARM Architecture, Automation of Tests, Microsoft Azure, Business Systems, Cloud Database, Code Review, Information Systems, Continuous Integration, Customer Data Management, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Security, Data Warehousing, Dimensional Modeling, Python (Programming Language), Role-Based Access Control, Microsoft Copilot, Salesforce.Com, SQL Databases, Data Streaming, Systems Integration, Snowflake, Git, Data Layers, Information Technology, Claude, GPT, Software Version Control - **Published:** October 23, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=2aa15b134e8799ae ## About the Role * 8+ years in data engineering or data architecture, including 2+ years as the technical lead for a data platform, a team, or a major data initiative, with a track record of balancing hands-on delivery with leading the work. * Deep, hands-on Snowflake experience: data modeling, tasks and dynamic tables, role-based access control, row access and masking policies, and cost management. * Expert SQL and dimensional modeling; working Python for pipelines and automation; experience with dbt or a similar transformation framework, Git, and CI/CD. * Experience designing row-level data access so that customers, offices, or business units see only their own data. * Financial services data experience; wealth or asset management is a plus. * Experience with semantic layers and AI data access such as Snowflake Cortex or semantic views preferred. * Experience integrating CRM data, especially Salesforce, with a data warehouse preferred. * Experience working with outsourced or vendor development teams preferred. * Familiarity with SEC and FINRA expectations for protecting client data preferred. Skills & Competencies * Turns business requirements into technical designs and specifications, and explains data trade-offs clearly to executives, business users, and engineers in writing, in conversation, and in diagrams. * Proactive and challenging of the status quo; asks probing questions to improve how data is managed and used. * Player-coach with the discipline to stay hands-on while setting standards and raising the capability of those around them. * Has managed people or is ready to grow into managing a small data team. Technology & Tools * Snowflake: advanced proficiency required across modeling, RBAC, row access policies, dynamic tables, and cost management. * Expert SQL, dimensional modeling, dbt or equivalent transformation framework, Git, and CI/CD. * Working Python for pipelines and automation. * Relevant certifications such as SnowPro Advanced (Architect or Data Engineer), CDMP, or a cloud data credential (Azure, AWS, or GCP) are a plus. * Proficiency with AI tools such as Claude, ChatGPT, or Microsoft Copilot to improve productivity, pipeline design, and data analysis workflows. Education * Bachelor's degree in Computer Science, Information Systems, or a related field; equivalent professional experience considered. * Master's degree is a plus. ## Description * Own our Snowflake environment: account structure, environments, compute, performance, and cost. * Run reliable pipelines from core business systems, including financial, CRM, and marketing platforms, with monitoring for freshness, completeness, and errors. * Put all data logic under version control with code review, automated testing, and a defined release process. * Define the target data architecture and roadmap, including reference architecture and standard integration patterns. * Tune performance, troubleshoot issues, and own backup, recovery, and retention for data assets. * Review new projects and requests for their data impact and produce high-level designs before work starts. * Simplify the data landscape: remove duplicate data and pipelines, and keep current diagrams of how data flows across systems. Building the Data Model & Metric Layer * Design and deliver a canonical data model for the firm's core business entities such as clients, accounts, and transactions. * Create a governed catalog of business metrics with clear definitions, owners, and tests. * Reconcile key figures to source systems and the general ledger, and publish the results. * Establish data governance: a data catalog with business definitions and lineage, data quality rules and monitoring, and named owners for key data. Client Data Protection * Design and maintain row-level access so each user and business unit sees only the data they are entitled to across every data surface, and test it regularly. * Lead classification, masking, and access reviews for client personal information in partnership with IT and Security. * Track changing privacy and data protection rules and partner with Compliance so our data practices stay ahead of them. AI Readiness * Build a documented semantic layer that AI assistants and internal tools can query safely. * Give AI and engineering teams governed datasets and access patterns so new tools can ship quickly. Migrations, Reporting & Partner Management * Lead the data work for system migrations. * Deliver the firm scorecard and leadership reporting on the same governed metrics used across the firm. * Hold technology vendors and development partners to clear data standards, and keep the firm able to move its data and logic between platforms. * Guide and upskill vendor engineers: set quality standards, review their work, and manage technical dependencies and roadmap milestones across teams.