{"id":347606,"url":"https://alion.io/job/sunset-backend-engineer-data-pipeline","title":"Backend Engineer — Data Pipeline","company":{"id":28307,"name":"Replay","domain":"replay.ai","url":"https://alion.io/company/replay","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":93,"open_postings":11,"ghost_share":0,"stale_share":0.273,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":"full_time","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":{"min":200000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"DynamoDB","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-09-04T21:16:48Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-10-31T22:29:58Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":56},"description":"About Replay\nWe are building the first-ever bank for enterprise data. There are over 35 million businesses in the US alone, many of which are sitting on an asset they don’t know they have. At Replay, we help businesses monetize their historical operating data by licensing it to the frontier AI and data labs for model training. Replay sits at the intersection of advancing the frontier of AI, while helping companies generate millions in growth capital without having to give up equity, take on debt, or lay off employees.\nWhy Join Replay Now\nWe have scaled from $0 to a multi-eight-figure run rate in a matter of months\n\nWe have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund\n\nWe are small enough that you will carry outsized responsibility and grow as quickly as the company does\n\nYou will partner with and build for some of the fastest and most important companies in the world\n\nYou will help build a massive, category-defining business from the ground floor\n\nThe Role\nWe're hiring a backend engineer to make the pipeline that de-identifies sensitive enterprise data correct, replayable, operable, and safe to change.\nThis is not a conventional data-platform role where a successful job is enough. A pipeline can finish while silently dropping records, duplicating output, applying stale policy, losing lineage, or producing evidence that cannot establish whether a dataset is safe to release. You will own the backend systems and contracts that make those failure modes visible, preventable, and recoverable.\nYou will work across asynchronous orchestration, batch workers, queues, object storage, databases, many file formats, model-backed stages, deterministic verification, and human review. The role is backend-focused, but the outcome is a product and delivery promise: the team must know what ran, what changed, what remains uncertain, and what can safely happen next.\nWhat You'll Own\nDesign and ship backend systems for multi-stage, high-volume data processing\n\nDefine authoritative, versioned contracts for manifests, artifacts, lineage, and state transitions\n\nMake retries, checkpoints, partial failures, replay, backfills, migrations, and rollbacks safe and understandable\n\nBuild independent reconciliation and verification instead of treating job success as proof of correct output\n\nTurn escaped and recurring failures into fixtures, regression coverage, release gates, and durable recovery paths\n\nExpose trustworthy run state and safe controls to the products people use to investigate and release data\n\nDiagnose production behavior across code, queues, stores, artifacts, data formats, and deployed versions\n\nImprove correctness, throughput, and operating leverage without weakening privacy, security, or release confidence\n\nUse AI deeply in development and in bounded verification systems, with explicit evaluation and independent checks\n\nPartner with machine learning, applied science, full-stack product, platform, security, and data engineering teammates\n\nWhat Success Looks Like\nA high-risk pipeline boundary has an explicit contract, independent reconciliation, replayable coverage, and safe recovery\n\nMissing, duplicated, stale, or incompatible work is detected before it becomes a customer delivery\n\nMaterial pipeline state and release decisions are backed by queryable provenance and audit evidence\n\nRecurring reruns, manual interventions, and diagnosis or recovery time decline\n\nAdjacent engineers can add stages and checks through supported patterns instead of one-off scripts and implicit storage conventions\n\nPipeline changes can be rolled out, backfilled, quarantined, or reversed without delivery heroics\n\nYou Might Thrive Here If\nYou have at least three years of professional software engineering experience, including personal ownership of production backend systems\n\nYou are strong in asynchronous or distributed systems and can reason precisely about queues, concurrency, state, storage, idempotency, partial failure, and recovery\n\nYou have worked on systems where output could be materially wrong even when every service looked healthy\n\nYou define invariants and use reconciliation, control totals, diffs, replay, goldens, shadow paths, or independent sources to verify correctness\n\nYou can design versioned data and artifact contracts and migrate them safely in a live system\n\nYou debug from evidence across system boundaries and turn incidents into durable system improvements\n\nYou choose technical work based on operator and customer consequences, not architecture in isolation\n\nYou use modern AI engineering tools fluently, verify their output, and know when model-backed checks need deterministic guardrails and human review\n\nYou communicate clearly across product, ML, data, platform, security, and customer-facing teams\n\nThis Role May Not Be for You If\nYou want to focus primarily on frontend product development or visual craft\n\nYou treat pipeline success, uptime, latency, or a green dashboard as sufficient evidence that the output is correct\n\nYou prefer isolated infrastructure work without responsibility for data and delivery consequences\n\nYou solve partial failure primarily with retries and manual runbooks\n\nYou do not want AI tools to be part of your daily engineering workflow\n\nBonus\nExperience with large-scale batch processing, workflow orchestration, event-driven systems, or data movement\n\nExperience with schema evolution, manifests, lineage, CDC, migrations, reindexing, or backfills\n\nExperience in payments, ledgers, reconciliation, claims, fraud, identity, search quality, observability, or another domain with delayed or weak ground truth\n\nExperience with sensitive or multi-tenant data, least-privilege systems, auditability, quarantine, and fail-closed release paths\n\nExperience combining deterministic checks, synthetic fixtures, offline replay, model-based judges, and human review\n\nExperience with Python, AWS, Airflow, Batch, SQS, S3, DynamoDB, PostgreSQL, or comparable systems","description_format":"text","description_chars":5984,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-05T18:42:00Z"},{"event":"reopen","at":"2026-10-12T17:15:17Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 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