{"id":2767759,"url":"https://alion.io/job/mphasis-senior-principal-enterprise-architect","title":"Senior Principal Enterprise Architect","company":{"id":59446,"name":"Mphasis","domain":"mphasis.com","url":"https://alion.io/company/mphasis","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"RippleHire","truth_index":{"grade":"B","score":78,"open_postings":20,"ghost_share":0,"stale_share":0.9,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Solutions","role_family":"Solutions","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":121000,"max_usd":248000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":556},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Cortex","optional":false},{"name":"LLM","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-09-18T00:00:00Z","employer_posted_date":"2026-10-17","last_verified_at":"2026-10-26T20:50:13Z","board_verified":true,"closed_at":null,"days_open":39,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":39},"description":"Experience: Sr. Data Architect with 10 – 15 years of work experience.\nLocation: New York, NY\nRole Title – Data Architect\nWe are seeking an experienced Snowflake Architect with strong Snowflake Cortex AI and agentic-solution experience to lead the architecture and delivery of governed Finance data and AI capabilities. The role will connect Finance business meaning, semantic models and Snowflake data with Cortex Skills, agents, reusable tools and Talk-to-Data experiences. The architect will define scalable solution patterns, guide engineering teams and establish the quality, security, lifecycle and operational controls required for production adoption.\nJob Functions/Duties and Responsibilities:\n·Define the end-to-end architecture for Snowflake-based Finance data, semantic and AI solutions across reporting, analytics and conversational use cases.\n··Design Cortex Skills, Cortex Agents, reusable agent tools, skill packages and governed Talk-to-Data capabilities.\n··Architect how AI agents consume approved semantic models, verified queries, metadata, business definitions and Finance context.\n·Define logical, dimensional, physical and semantic modelling patterns for Finance domains such as General Ledger, P&L, Revenue and Spend.\n·Create reusable design patterns for MTD, QTD, YTD and other dynamic Finance calculations, including the appropriate use of Snowflake Dynamic Tables.\n·Guide the implementation of Snowflake data-consumption layers, transformation patterns, data services and APIs supporting dashboards and AI experiences.\n··Establish evaluation frameworks using golden question-and-answer sets, regression testing, confidence scoring, abstention and user-feedback loops.\n·Define lifecycle controls for semantic assets, Cortex Skills, agents, prompts, tools, metadata and verified queries.\n·Embed identity, entitlements, data protection, auditability, prompt-injection controls and responsible-AI requirements into the solution design.\n·Define observability for quality, accuracy, latency, usage, drift, failures and operational performance, together with recovery workflows and runbooks.\n·Partner with Finance SMEs, Product Owners, data owners, governance teams, security reviewers and engineers to translate business needs into an approved architecture and delivery backlog.\n··Provide technical leadership through architecture reviews, design decisions, code reviews, mentoring and resolution of complex performance or integration issues.\n··Support production-readiness, knowledge transfer and controlled scale-up of reusable Finance AI capabilities.\nSkills Required:\n·10-15 years of overall experience in data architecture, data engineering, data warehousing, analytics or AI solution delivery.\n·Strong hands-on architecture and implementation experience on Snowflake, including advanced SQL, data modelling, performance optimisation and secure data consumption.\n·Demonstrable experience with Snowflake Cortex, Cortex Skills, agentic AI solutions, LLM applications or conversational analytics.\n·Prior delivery experience in the Finance domain, with practical understanding of General Ledger, P&L, Revenue, Spend, financial measures, controls or management reporting.\n··Experience integrating semantic models, metadata, verified queries and governed business context into AI or Talk-to-Data solutions.\n··Experience designing evaluation, monitoring, confidence, feedback and lifecycle-control patterns for enterprise AI solutions.\nProven ability to lead architecture discussions and work with business, data, security, governance and engineering stakeholders in a regulated environment.\nSuccess Measures:\n·Finance AI use cases are grounded on governed Snowflake data, approved semantic definitions and appropriate access controls.\n··Cortex Skills and agents meet agreed thresholds for accuracy, confidence, traceability, security and operational readiness.\n··Reusable architecture and calculation patterns reduce one-off implementation and improve consistency across Finance use cases.\n·Engineering teams receive clear, implementable designs and timely architectural decisions.\n·Solutions meet agreed performance, reliability, data-quality, auditability and production-support expectations.\nEducational Qualification:\n·Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, Data Science or a related discipline.\n·SnowPro Advanced: Architect, SnowPro Core or a 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