{"id":184579,"url":"https://alion.io/job/applied-retention-founding-data-backend-engineer","title":"Founding Data & Backend Engineer","company":{"id":19453,"name":"Applied Retention","domain":"appliedretention.com","url":"https://alion.io/company/applied-retention","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16000,"max_usd":37000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":159},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Apache Kafka","optional":true},{"name":"Django","optional":true}],"status":"live","first_seen_at":"2026-09-01T09:37:17Z","employer_posted_date":null,"last_verified_at":"2026-09-01T09:37:17Z","board_verified":false,"closed_at":null,"days_open":52,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":52},"description":"You own the data substrate and the batch runtime of a product we are building from scratch. This is a data-specialist seat, not a general backend seat. Routine application development is guided in weekly reviews; what cannot be substituted is your depth in data modelling, set-based computation, and pipeline correctness. You will work self-directed, writing your own specs and decisions, with your tests and documentation carrying the quality bar between reviews.\nResponsibilities:\nThird-party data ingestion, done deeply. Webhooks drop silently, so reconciliation against source-of-truth totals is not optional.\nBatch computation over large datasets as set-based SQL, inside fixed nightly windows.\nMulti-tenant isolation as a reliability property. One tenant's bad data must never degrade another tenant's run.\nBackfill and replay as designed capabilities, not emergency scripts.\nAppend-only, auditable records. Every automated decision must be reconstructable.\nVendor API integrations built for failure: circuit breakers, retries, reconciliation.\nThe operational substrate: alerting, partitioning, idempotent jobs.\nRequirements:\nAround 5 to 8 years building and operating production systems. Years are a proxy; the points below are the bar.\nDeep Postgres: schema design, query plans, batch performance at real scale.\nYou have owned data pipelines in production: ingestion, transformation, reconciliation, serving. Not application CRUD against an ORM. Moving and reshaping data is the job you have actually done, whatever your resume calls it.\nYou think in sets, not loops. Multi-step SQL transformations, window functions, statistical aggregates (percentiles, distributions), and incremental computation are your native mode, and you have turned raw event or order data into cohort, retention, or funnel metrics.\nProduction Python backend experience in any framework. You keep business logic cleanly separated from framework details, which is exactly why the framework does not matter.\nTests are how you build, not what you add later. Batch jobs and reconciliations especially, where a bug means silently wrong data for weeks.\nWar stories: queues backing up, webhooks silently dropping, reconciliations catching data loss nobody noticed.\nStrong written communication. This is a documentation-driven team, and the way we build runs on written specs and decisions.\nComfortable owning outcomes and building from scratch with minimal supervision.\nGood to have:\nProduction Django.\nE-commerce or D2C platform experience.\nStreaming or event-architecture experience (Kafka-class systems).\nFamiliarity with probabilistic data structures (HyperLogLog, Bloom filters, t-digest).\nComfort building the occasional server-rendered screen.\nStack: Python, PostgreSQL, Django, server-rendered frontend (htmx).","description_format":"text","description_chars":2794,"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":[],"lifecycle":[{"event":"open","at":"2026-09-01T11:49:17Z"}],"visa":[],"liveness":{"score":10,"band":"cold","label":"Long shot","p_open":0.4,"p_active":0.565,"p_room":0.45,"age_days":51,"expected_fill_days":27,"reasons":["seen:51","win:tail"],"computed_at":"2026-10-23T05:54:15Z"},"pay":null,"html_url":"https://alion.io/job/applied-retention-founding-data-backend-engineer","json_url":"https://alion.io/job/applied-retention-founding-data-backend-engineer.json","meta":{"generated_at":"2026-10-23T15:13:02Z","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":22898,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-24T00:00:00Z"}},"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":19453},"rest":"https://alion.io/mcp/rest/get_company?id=19453"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fapplied-retention-founding-data-backend-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%2Fapplied-retention-founding-data-backend-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%2Fapplied-retention-founding-data-backend-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/applied-retention-founding-data-backend-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fapplied-retention-founding-data-backend-engineer"}]}