{"id":2704562,"url":"https://alion.io/job/wex-database-architect","title":"Database Architect","company":{"id":2019,"name":"WEX","domain":"wexinc.com","url":"https://alion.io/company/wex","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":121000,"max_usd":208000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":104},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"Azure SQL Database","optional":false},{"name":"Bicep","optional":false},{"name":"C#","optional":false},{"name":"Claude Code","optional":false},{"name":"CloudFormation","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"HIPAA","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Java","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"Milvus","optional":false},{"name":"MS SQL","optional":false},{"name":"PCI DSS","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Weaviate","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"Neo4j","optional":true}],"status":"live","first_seen_at":"2026-10-15T00:00:00Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-17T02:42:36Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Role Scope\nOwns data architecture across multiple domains, with dual focus: (1) modernizing legacy database systems-particularly large-scale SQL Server environments with complex stored procedures containing embedded business logic, and (2) designing AI-native data infrastructure-semantic data models, vector databases, and RAG architectures that power AI agents and workflows. Establishes patterns for domain-driven data models, event-driven data flows, and canonical schemas across relational, NoSQL, analytical, and AI-optimized data platforms.\nThe Problem We're Solving\nWe have two interconnected data architecture challenges.\nLegacy modernization: SQL Server stored procedures that have grown to thousands of lines, with business logic deeply embedded in T-SQL that should live in application services. These procedures are difficult to test, impossible to reuse, and create tight coupling between business rules and data access. They slow down development, complicate domain ownership, and resist modernization efforts.\nAI data infrastructure: We're building AI agents and agentic workflows that require purpose-built data architecture-semantic models that capture domain knowledge, vector databases for retrieval-augmented generation (RAG), embedding pipelines, and knowledge graphs that give AI systems the context they need to operate effectively. This isn't about bolting AI onto existing data systems; it's about designing data infrastructure that makes AI-native applications possible.\nThese challenges intersect: the domain knowledge locked in legacy systems becomes training data and context for AI agents. The canonical models we build for modernization become the semantic foundation for AI retrieval. We need database architects who can work across both dimensions-modernizing legacy while building for an AI-native future.\nThe Opportunity\nWe are seeking a Senior Database Architect who combines deep expertise in legacy database systems with forward-looking vision for AI-native data architecture. You'll lead the decomposition of complex stored procedures while simultaneously designing the vector databases, embedding strategies, and semantic models that power our AI agents and workflows.\nThis is an AI-first role in two senses: you'll leverage AI to accelerate your own work (stored procedure analysis, migration generation, schema documentation), and you'll design the data infrastructure that AI systems depend on. If you're excited about both solving hard legacy database problems and architecting the data layer for AI-native applications, this role is for you.\nWhat You'll Do\nLegacy Database Modernization\nAnalyze and decompose large SQL Server stored procedures (1,000+ lines) with embedded business logic, creating migration strategies that extract logic into domain services\n\nDesign patterns for separating business rules from data access, enabling stored procedures to become thin data-access layers while business logic moves to application services\n\nLead refactoring efforts that align database structures with domain-driven design: bounded contexts, aggregates, and domain events\n\nImplement event-driven patterns that decouple systems from direct database dependencies: change data capture, outbox patterns, event sourcing where appropriate\n\nOptimize query performance, indexing strategies, and execution plans as part of modernization efforts\n\nCreate migration playbooks and tooling that engineering teams can apply to their own stored procedure modernization\n\nAI Data Infrastructure & Semantic Modeling\nDesign semantic data models that capture domain knowledge in structures optimized for AI retrieval and reasoning\n\nArchitect vector database solutions for RAG implementations: embedding strategies, chunking approaches, similarity search optimization, and hybrid retrieval patterns\n\nDesign and implement embedding pipelines that transform domain content into vector representations suitable for AI agent consumption\n\nEstablish knowledge graph patterns where appropriate: entity relationships, ontologies, and graph-based retrieval for complex domain reasoning\n\nDefine data architectures for AI agent context: what data agents need, how it's structured, how freshness and consistency are maintained\n\nDesign evaluation frameworks for RAG quality: retrieval accuracy, relevance scoring, and feedback loops for continuous improvement\n\nModern Data Platform Architecture\nDesign canonical data models and schemas that are flexible, extensible, and aligned with business domain concepts\n\nArchitect data solutions across multiple platforms: SQL Server, PostgreSQL, MongoDB/Cosmos DB, Snowflake, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search)\n\nDesign event-driven data flows: Kafka-based event streaming, materialized views, CQRS patterns, and real-time data synchronization\n\nEstablish data platform infrastructure patterns: data pipelines, ETL/ELT orchestration, data quality frameworks, and observability\n\nDefine data residency, partitioning, and multi-region strategies for performance and compliance\n\nCreate reference architectures for common data patterns that domain teams can adopt\n\nAI-First Database Engineering\nLeverage AI coding assistants (GitHub Copilot, Cursor, Claude Code) to accelerate stored procedure analysis, refactoring, and migration\n\nBuild AI-powered tools for database engineering: automated stored procedure analysis, schema documentation generators, migration assistants, and query optimization recommenders\n\nCreate AI-consumable artifacts: structured documentation, annotated schemas, and context files that enable AI agents to understand and work with database systems\n\nAuthor database architecture skills that encode patterns, constraints, and best practices for AI-assisted development\n\nDevelop prompts, workflows, and tooling that help engineering teams apply AI effectively to database modernization tasks\n\nCross-Domain Leadership\nPartner with AI/ML teams to ensure data architecture supports agent and workflow requirements\n\nCollaborate with domain teams to understand their data requirements and design solutions aligned with domain ownership\n\nWork with application architects to ensure data architecture supports service-oriented and event-driven designs\n\nContribute to Enterprise Architecture Council (EAC) standards for data architecture, modeling conventions, and technology selection\n\nMentor engineers on database design, optimization, semantic modeling, and AI data infrastructure\n\nWhat You'll Bring\nRequired Experience\n8-12 years in database engineering and architecture, with significant experience in enterprise-scale SQL Server environments\n\nDeep SQL Server expertise: T-SQL optimization, stored procedure design and refactoring, query plan analysis, indexing strategies, and performance tuning\n\nHands-on modernization experience: track record of decomposing complex stored procedures and migrating business logic to application services\n\nMulti-platform data architecture: experience designing solutions across relational (SQL Server, PostgreSQL), NoSQL (MongoDB, Cosmos DB), and analytical (Snowflake, data lakehouse) platforms\n\nEvent-driven data patterns: CDC, Kafka, outbox pattern, event sourcing, CQRS-practical experience implementing these in production\n\nData modeling expertise: canonical models, dimensional modeling, schema evolution, and designing for extensibility\n\nAI & Semantic Data Competencies\nVector database experience: hands-on with at least one vector DB (Pinecone, Weaviate, Milvus, pgvector, Azure AI Search, or similar)\n\nRAG architecture understanding: embedding models, chunking strategies, retrieval optimization, hybrid search, and reranking patterns\n\nSemantic modeling: experience designing data structures optimized for AI retrieval-knowledge representation, ontologies, or domain-specific schemas for AI consumption\n\nUnderstanding of embedding pipelines: text preprocessing, embedding generation, vector indexing, and incremental updates\n\nFamiliarity with LLM context requirements: what data AI agents need, token constraints, context window optimization\n\nAI-Native Engineering Practices\n2+ years actively using AI coding assistants for database work; deep understanding of how to prompt effectively for SQL and data engineering tasks\n\nExperience building tools, scripts, or automation that leverage AI/LLM capabilities\n\nFamiliarity with structured artifact creation for AI consumption: documented schemas, annotated procedures, context files\n\nVision for AI-assisted database engineering and ability to build tooling that enables it\n\nTechnical Depth\nStrong programming skills in at least one backend language (C#, Java, Python) for building migration tooling, embedding pipelines, and services\n\nCloud data services experience: Azure SQL, Cosmos DB, Azure AI Search, Azure Synapse, Snowflake, or AWS equivalents\n\nInfrastructure-as-code for data platforms: Terraform, ARM/Bicep, or CloudFormation\n\nUnderstanding of domain-driven design and how data architecture supports bounded contexts\n\nFamiliarity with data governance, lineage, and compliance requirements (HIPAA, PCI-DSS)\n\nPreferred Experience\nBackground in healthcare, benefits, payments, or similarly regulated industries\n\nExperience building RAG systems or AI-powered search/retrieval applications\n\nKnowledge graph experience: Neo4j, Amazon Neptune, or similar graph databases\n\nContributions to database tooling, AI/ML data infrastructure, or open-source projects\n\nExperience mentoring engineers or leading database/data architecture communities of practice\n\nWhat Success Looks Like\nIn 90 days: Completed assessment of priority stored procedure modernization targets and AI data infrastructure needs; delivered first AI-assisted analysis tooling; established vector database patterns for initial RAG implementations\nIn 6 months: Led decomposition of at least one major stored procedure system; semantic data models and RAG architecture patterns established and being adopted; AI-powered database engineering tools in active use by teams\nIn 12 months: Measurable reduction in stored procedure complexity across priority systems; AI data infrastructure supporting production agent workflows; recognized as the go-to expert for both database modernization and AI-native data architecture\nWhy This Role Matters\nData architecture is being transformed from two directions simultaneously.\nFrom the legacy side: business logic buried in stored procedures creates invisible dependencies that resist refactoring. Traditional approaches to database modernization are slow, manual, and error-prone-but AI can analyze thousands of lines of T-SQL, identify patterns, and accelerate migrations in ways that weren't possible before.\nFrom the AI side: agents and workflows need purpose-built data infrastructure. The semantic models, vector databases, and knowledge representations you design will determine how effectively AI can reason about our domains. This isn't a nice-to-have capability; it's foundational to our AI-native engineering strategy.\nYou'll work at the intersection of these transformations-solving hard legacy problems while building the data infrastructure that makes AI-native applications possible. The patterns you establish will shape how we approach data architecture across the enterprise.\nThe base pay range represents the anticipated low and high end of the pay range for this position. Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role. Base pay is one component of WEX's total compensation package. Most sales positions are eligible for commission under the terms of an applicable plan. Non-sales roles are typically eligible for a quarterly or annual bonus based on their role and applicable plan. WEX's comprehensive and market competitive benefits are designed to support your personal and professional well-being. Benefits include health, dental and vision insurances, retirement savings plan, paid time off, h...","description_format":"text","description_chars":12213,"description_truncated":true,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Life insurance","Retirement plans","Vision insurance"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Fleet Management","Cards & Card Issuing","Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-10-16T12:11:36Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":23,"reasons":["conf:3","velocity","win:early","comp:brand"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/wex-database-architect","json_url":"https://alion.io/job/wex-database-architect.json","meta":{"generated_at":"2026-10-18T06:40:52Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":9942,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-19T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2019},"rest":"https://alion.io/mcp/rest/get_company?id=2019"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fwex-database-architect"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fwex-database-architect"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fwex-database-architect"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/wex-database-architect\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fwex-database-architect"}]}