{"id":2325760,"url":"https://alion.io/job/fello-staff-engineer-data-architecture-gtm-ai-platform","title":"Staff Engineer, Data Architecture & GTM AI Platform","company":{"id":185227,"name":"Fello","domain":"fello.ai","url":"https://alion.io/company/fello","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-19T05:45:15Z"}},"role":"Sales","role_family":"Sales","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":"explicit","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":52000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":814},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"PostgreSQL","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Supabase","optional":false},{"name":"Tool Use","optional":false},{"name":"Anthropic","optional":true},{"name":"OpenAI","optional":true},{"name":"SOC 2","optional":true}],"status":"live","first_seen_at":"2026-09-30T19:01:57Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-19T23:38:10Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"description":"About Fello:\nFello is a profitable, hyper-growth, VC-backed B2B SaaS company building the agentic real estate platform of the future.\nOur platform combines data intelligence, marketing automation, and conversational AI to help real estate teams engage smarter and scale faster. At the center of it is Felix, our AI teammate, which takes real work off a team's plate - running follow-up across calls, texts, and emails today, with more of the team's day-to-day work coming next.\nWe're not another tool in the stack. We're the system that powers it - learning, adapting, and executing in real time so teams can focus on growth, not guesswork.\nIf you're excited about building an AI product that's changing how an entire industry works, you'll fit right in.\nAbout You:\nYou're a software engineer first. You spent years building and scaling production systems before LLMs made it easy, and a big part of that time was on database, data platform, or infra problems, not a one-year detour. You've since gone deep on AI: agents, tool use, structured outputs, retrieval, memory. You know where a schema, a rule, or a small model beats a prompt, and you don't ship coding-agent output you can't explain.\nYou're opinionated about structure. When the team wants to ship a new definition of a field that already exists, you're the one who says \"no, we use the canonical one, here's why.\" You're comfortable being the person who slows things down for the right reasons, and you explain your reasoning well enough that people start thinking that way themselves.\nYou want to stay deep in the technical work rather than move into pure management. You're happy juggling several products at once without a roadmap handed to you, and you give and take blunt technical feedback without it becoming a thing.\nYou Will:\nOwn the data architecture and software infrastructure across all internal GTM apps. That means schema design, object modeling, access patterns, and the path from raw source to what an agent or user reads.\nDesign our data system for scale. Every app keeps metadata ledgers recording the history behind its data: who did what, where a lead came from, what an agent changed. Agents read these ledgers. You'll decide what lives in hot storage and what goes to low-cost cold storage, and how it stays queryable for debugging and audit.\nBridge R&D. Take systems built fast in R&D and re-architect to reconfigure whatever doesn't fit infrastructure’s constraints. You'll be the quality gate for what goes to production.\nDesign for scale before it hurts. That includes caching layers, indexing and fast read paths, streaming and queueing instead of hammering the DB, and event-driven agent runs instead of nightly crons. You'll keep an active eye on latency, query cost, and token spend.\nBuild and enforce a data dictionary. Every entity and metric (account health, sentiment score, risk, lifecycle stage) gets one canonical, versioned, deterministic definition that every app reuses. No duplicate definitions and no metrics that are just an LLM's opinion.\nGive agents proper data access. Build SQL and query tools, MCP servers, and endpoints so agents fetch exactly what they need instead of dumping data into context. That's how we cut both cost and hallucination.\nOwn access and security. Set up RBAC, per-team and per-app views, tenant isolation, PII handling, and secrets management, so customer data only goes where it should.\nBuild and harden agents that execute real workflows, with guardrails: prompt injection defense at the tool boundary, human-in-the-loop on anything customer-facing, validated and cited output, and evals that catch regressions before customers do.\nMake the deterministic-vs-probabilistic call on every system, and ship whichever side wins.\nSet the engineering bar. Architecture docs come before implementation. You'll run reviews that teach, write down reusable patterns, and keep runbooks so the same problem never gets solved twice.\nMentor a fast, young, AI-native team. They ship quickly and lean hard on coding agents. Your job is to make sure they understand the systems they're generating and start thinking in objects, schemas, and contracts.\nWork across multiple products at once (CS, sales, marketing, product) and keep the architecture coherent across all of them.\nYou Have:\n7+ years of software engineering, mostly pre-LLM. You've built systems from the ground up, not just wired existing ones together.\nDeep data architecture experience. You've modelled structured and unstructured data, designed warehouses and data lakes, built ETL/ELT pipelines, and designed schemas that held up as the product grew.\nReal distributed backend experience. Services that carried significant load, with caching, streaming (Kafka, Pub/Sub or similar), and queueing, and the lessons that come from running them.\nHands-on cloud depth, ideally AWS. Not just using it, but knowing which services to pick for which data (Athena, S3 tiers, RDS/Postgres and so on) and how to tune them for speed and cost.\nStrong Postgres skills, including performance tuning, indexing, and query design. Supabase is a plus.\nHands-on production experience with modern LLMs (Anthropic, OpenAI, open models): agents, copilots, or workflows you shipped and maintained.\nReliability instincts: structured prompting, output validation, evals, observability.\nA security posture that isn't an afterthought: authz, RBAC, secrets, PII, tenant isolation, and awareness of SOC 2 expectations for a B2B SaaS platform.\nExperience leading or mentoring engineers, with a track record of raising the floor of a team.\nNice to have: time on a database, data platform, or infra team at scale; having designed a monorepo or internal platform other teams build on; chat-first assistants that take real actions; small language models or decision models replacing LLM calls on high-volume tasks; agent observability and evals in production; vector search and retrieval at scale; CRM and revenue ops data models; real estate or proptech context.\nOur Benefits:\nCompetitive Compensation: Attractive salary and benefits package.\nFlexible Work Environment: Fully remote work with flexible hours to promote work-life balance.\nProfessional Growth: Opportunities for career advancement and professional development.\nHealth & Wellness: Comprehensive health and vision insurance plans.\nPaid Time Off: Generous PTO and paid holidays to recharge and relax.\nCollaborative Culture: A supportive team environment that values innovation and collaboration.\nEquity Options: Opportunity to own a part of Fello and share in our success.\nCutting-Edge Projects: Work on innovative products that leverage AI and advanced technologies.","description_format":"text","description_chars":6677,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Flexible schedule","Professional development","Vision insurance"],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Industrial Real Estate","Marketing & Sales AI","Real Estate AI"],"lifecycle":[{"event":"open","at":"2026-10-12T07:17:02Z"}],"visa":[],"liveness":{"score":77,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":0.9,"age_days":18,"expected_fill_days":44,"reasons":["conf:1","win:mid"],"computed_at":"2026-10-19T05:45:15Z"},"pay":null,"html_url":"https://alion.io/job/fello-staff-engineer-data-architecture-gtm-ai-platform","json_url":"https://alion.io/job/fello-staff-engineer-data-architecture-gtm-ai-platform.json","meta":{"generated_at":"2026-10-20T01:34:04Z","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":1965,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-21T00:00:00Z"}},"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":185227},"rest":"https://alion.io/mcp/rest/get_company?id=185227"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ffello-staff-engineer-data-architecture-gtm-ai-platform"},{"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%2Ffello-staff-engineer-data-architecture-gtm-ai-platform"},{"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%2Ffello-staff-engineer-data-architecture-gtm-ai-platform"},{"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/fello-staff-engineer-data-architecture-gtm-ai-platform\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ffello-staff-engineer-data-architecture-gtm-ai-platform"}]}