{"id":2070667,"url":"https://alion.io/job/wex-database-engineer","title":"Database Engineer","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":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":44000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":54},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"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":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Edge AI","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"MS SQL","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Weaviate","optional":false},{"name":"Oracle","optional":true}],"status":"live","first_seen_at":"2026-10-08T06:45:00Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-09T23:54:02Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About the team / Role\nHands-on database engineering role focused on implementing, optimizing, and modernizing data systems across the technology stack. Works closely with Database Architects to execute modernization initiatives-refactoring stored procedures, building data pipelines, implementing vector databases, and developing AI-powered tooling. This is an execution-heavy role where you'll write code daily: T-SQL, Python, infrastructure-as-code, and whatever else is needed to get data systems working well.\nWe are seeking a Senior Database Engineer to join our data engineering team. You'll work on two fronts: modernizing legacy SQL Server systems (decomposing complex stored procedures, optimizing performance, migrating business logic to services) and building AI-native data infrastructure (embedding pipelines, vector database implementations, RAG components).\nThis is an AI-first engineering role. You'll use AI coding assistants daily to accelerate your work-analyzing stored procedures, generating migration code, debugging query performance issues. You'll also build the data infrastructure that AI agents depend on: the embedding pipelines, vector indexes, and retrieval systems that make RAG work.\nIf you enjoy the craft of database engineering-writing elegant queries, optimizing execution plans, building reliable pipelines-and want to apply those skills to both legacy modernization and cutting-edge AI infrastructure, this role is for you.\nHow you'll make an impact\nStored Procedure Refactoring & Legacy Modernization\nAnalyze complex SQL Server stored procedures to understand embedded business logic and data access patterns\nRefactor stored procedures following architect-defined patterns: extracting business logic, simplifying data access, improving testability\nWrite migration scripts that safely transform database structures while maintaining data integrity\nImplement event-driven patterns: change data capture (CDC), outbox tables, and event publishing from database changes\nOptimize query performance: analyze execution plans, design indexes, refactor inefficient queries\nBuild automated testing for database migrations and refactored procedures\nDocument database systems, creating AI-consumable artifacts (structured markdown, annotated schemas) alongside traditional documentation\nAI Data Infrastructure Implementation\nBuild and maintain embedding pipelines: text extraction, preprocessing, chunking, embedding generation, and vector storage\nImplement vector database solutions: configure indexes, optimize similarity search, implement hybrid retrieval patterns\nDevelop data synchronization processes that keep vector stores current with source systems\nBuild evaluation and monitoring for RAG components: retrieval accuracy, latency, freshness metrics\nImplement semantic search features and retrieval APIs that AI agents and applications consume\nWork with AI/ML teams to optimize embedding strategies and retrieval quality\nData Platform Engineering\nDesign and implement data pipelines for ETL/ELT workflows across SQL Server, PostgreSQL, Snowflake, and cloud data services\nBuild and maintain data integration patterns: API-based ingestion, event streaming, batch processing\nImplement data quality checks, validation rules, and observability for data pipelines\nDevelop infrastructure-as-code for database provisioning and configuration (Terraform, ARM/Bicep)\nSupport NoSQL implementations: MongoDB, Cosmos DB document modeling and query optimization\nImplement data access patterns that support domain-driven design: repository patterns, query services, read models\nAI-Assisted Engineering\nUse AI coding assistants (GitHub Copilot, Cursor, Claude Code) daily for stored procedure analysis, code generation, and debugging\nDevelop prompts, scripts, and workflows that leverage AI for database engineering tasks\nContribute to AI-powered tooling: stored procedure analyzers, schema documentation generators, migration assistants\nCreate AI-consumable artifacts: structured schemas, annotated procedures, context files for AI agents\nHelp evaluate and adopt new AI tooling for database engineering\nCollaboration & Quality\nPartner with application engineers to design data access patterns that meet performance and scalability requirements\nParticipate in code reviews for database-related changes, ensuring quality and consistency\nContribute to on-call rotation for data platform issues when applicable\nDocument solutions and contribute to team knowledge bases\nMentor junior engineers on database engineering practices\nExperience you will bring\n5-8 years in database engineering or data platform roles, with strong SQL Server experience\nDeep T-SQL proficiency: complex queries, stored procedures, functions, performance tuning, and execution plan analysis\nHands-on refactoring experience: you've modernized legacy database code, not just maintained it\nData pipeline experience: ETL/ELT development, data integration patterns, batch and streaming workflows\nProgramming proficiency: Python or C# for building tooling, automation, and data processing scripts\nCloud data services: experience with Azure SQL, Cosmos DB, Snowflake, or AWS data services\nAI & Vector Database Skills\nFamiliarity with vector databases: exposure to Pinecone, Weaviate, pgvector, Azure AI Search, or similar\nUnderstanding of embeddings and RAG concepts: how text becomes vectors, how similarity search works, basic retrieval patterns\nExperience with or willingness to learn embedding pipeline development\nActive use of AI coding assistants in daily work; understanding of effective prompting for database tasks\nInterest in building AI-powered tooling and automation\nTechnical Depth\nStrong understanding of database internals: indexing, query optimization, locking, transaction isolation\nExperience with event-driven patterns: CDC, Kafka, event sourcing concepts\nInfrastructure-as-code: Terraform, ARM templates, or similar for database provisioning\nVersion control and CI/CD for database changes: migrations, schema versioning, deployment automation\nFamiliarity with NoSQL: document databases, key-value stores, when to use what\nPreferred Experience\nBackground in healthcare, benefits, payments, or similarly regulated industries\nExperience with Oracle PL/SQL in addition to SQL Server\nHands-on RAG implementation or semantic search development\nContributions to database tooling or open-source data projects\nExperience with data observability tools: query monitoring, performance dashboards, alerting\nIn 90 days: Onboarded to primary database systems; completed first stored procedure refactoring project; built initial embedding pipeline or vector database implementation; actively using AI tools in daily work\nIn 6 months: Independently leading stored procedure modernization for assigned systems; RAG/vector infrastructure you've built is in production use; contributing to AI-powered database tooling; recognized by team as go-to for complex database problems\nIn 12 months: Measurable impact on stored procedure modernization velocity; AI data infrastructure supporting production agent workflows; mentoring junior engineers; contributing to architectural patterns and standards\nWhy This Role Matters\nDatabase engineering is at an inflection point. Legacy systems need modernization-but we can now use AI to analyze, understand, and migrate complex database code faster than ever. AI applications need purpose-built data infrastructure-and database engineers who understand both traditional data systems and vector/embedding technologies are rare.\nYou'll work on both sides: using AI to accelerate legacy modernization while building the data layer that AI applications depend on. The skills you develop here-combining deep database craft with AI-native infrastructure-will be increasingly valuable as every organization grapples with these same challenges.","description_format":"text","description_chars":7855,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Fleet Management","Cards & Card Issuing","Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-10-08T06:45:00Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":29,"reasons":["conf:1","velocity","win:early","comp:brand"],"computed_at":"2026-10-09T06:01:00Z"},"pay":null,"html_url":"https://alion.io/job/wex-database-engineer","json_url":"https://alion.io/job/wex-database-engineer.json","meta":{"generated_at":"2026-10-10T01:59:24Z","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","counted_by":"address","units_charged":1,"used_today":3500,"day_limit":5000,"remaining_today":1500,"minute_limit":60,"resets_at":"2026-10-11T00:00:00Z"}},"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-engineer"},{"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-engineer"},{"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-engineer"},{"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-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fwex-database-engineer"}]}