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# Senior Backend Engineer

- **Company:** Wintermeyer Ventures
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $220,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Infrastructure, Software Debugging, Amazon DynamoDB, Python (Programming Language), PostgreSQL, Machine Learning, Software Engineering, Workflow Management Systems, Data Processing, Concurrency, Backend, Amazon Simple Queue Service (SQS), Data Pipelines
- **Published:** October 20, 2026
- **Apply:** https://www.careerjet.com/jobad/us8ed67fc62d94068d9f2ed531579e9a8b

## About the Role

* 3 to 10 years of software engineering experience, including personal ownership of production backend systems.
 * Experience building and operating high-volume data processing systems that transform data, not only move it between systems.
 * Strong distributed and asynchronous systems fundamentals, including queues, workers, concurrency, idempotency, retries, and recovery from partial failures.
 * Backend depth and strong experience in a general-purpose language such as Python, Go, Rust, or Java.
 * Experience with AWS and batch or workflow orchestration. Relevant technologies include Airflow, AWS Batch, SQS, S3, DynamoDB, and PostgreSQL.
 * Meaningful early-stage or high-growth startup experience, a STEM undergraduate degree, and a track record of ownership and progression.

## Description

Join a small engineering team at an AI data infrastructure company to build and operate backend systems that prepare sensitive enterprise data for AI training. You will help ensure high-volume data pipelines are reliable, auditable, and safe to recover when failures occur.
What You'll Do

 * Build and scale multi-stage pipelines that de-identify enterprise data from communication, email, document, code, and project management sources.
 * Own backend systems from design and delivery through debugging, correctness checks, and recovery.
 * Make retries, checkpoints, partial failures, replay, backfills, and rollbacks safe across asynchronous workflows, batch workers, queues, and object storage.
 * Build internal tools and dashboards to monitor pipeline performance, investigate data quality, and support release decisions with queryable provenance.
 * Work with machine learning, full-stack, platform, and security teammates to support new data types and pipeline capabilities.