Backend Engineer — Data Pipeline

Sunset•New York, NY

2d•$200,000 - $200,000

About The Position

At its core, Sunset was founded to help founders. We started by supporting startups through shutting down, but we have since expanded into unlocking a new revenue stream for all types of businesses. In 2025, we had a unique insight: the data every company generates each day through collaboration, communication, and building is some of the most valuable training data in the world. Public and synthetic data can only get frontier models so far, so the next generation of model progress depends on real, proprietary data grounded in how actual businesses operate. We are a primary source of it, partnering directly with the frontier AI labs building what comes next. We have scaled from $0 to a multi-eight-figure run rate in a matter of months We have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund We are small enough that you will carry outsized responsibility and grow as quickly as the company does You will partner with and build for some of the fastest and most important companies in the world You will help build a massive, category-defining business from the ground floor. We're hiring a backend engineer to make the pipeline that de-identifies sensitive enterprise data correct, replayable, operable, and safe to change. This 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. You 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.

Requirements

At least three years of professional software engineering experience, including personal ownership of production backend systems
Strong in asynchronous or distributed systems and can reason precisely about queues, concurrency, state, storage, idempotency, partial failure, and recovery
Worked on systems where output could be materially wrong even when every service looked healthy
Define invariants and use reconciliation, control totals, diffs, replay, goldens, shadow paths, or independent sources to verify correctness
Can design versioned data and artifact contracts and migrate them safely in a live system
Debug from evidence across system boundaries and turn incidents into durable system improvements
Choose technical work based on operator and customer consequences, not architecture in isolation
Use modern AI engineering tools fluently, verify their output, and know when model-backed checks need deterministic guardrails and human review
Communicate clearly across product, ML, data, platform, security, and customer-facing teams

Nice To Haves

Experience with large-scale batch processing, workflow orchestration, event-driven systems, or data movement
Experience with schema evolution, manifests, lineage, CDC, migrations, reindexing, or backfills
Experience in payments, ledgers, reconciliation, claims, fraud, identity, search quality, observability, or another domain with delayed or weak ground truth
Experience with sensitive or multi-tenant data, least-privilege systems, auditability, quarantine, and fail-closed release paths
Experience combining deterministic checks, synthetic fixtures, offline replay, model-based judges, and human review
Experience with Python, AWS, Airflow, Batch, SQS, S3, DynamoDB, PostgreSQL, or comparable systems

Responsibilities

Design and ship backend systems for multi-stage, high-volume data processing
Define authoritative, versioned contracts for manifests, artifacts, lineage, and state transitions
Make retries, checkpoints, partial failures, replay, backfills, migrations, and rollbacks safe and understandable
Build independent reconciliation and verification instead of treating job success as proof of correct output
Turn escaped and recurring failures into fixtures, regression coverage, release gates, and durable recovery paths
Expose trustworthy run state and safe controls to the products people use to investigate and release data
Diagnose production behavior across code, queues, stores, artifacts, data formats, and deployed versions
Improve correctness, throughput, and operating leverage without weakening privacy, security, or release confidence
Use AI deeply in development and in bounded verification systems, with explicit evaluation and independent checks
Partner with machine learning, applied science, full-stack product, platform, security, and data engineering teammates