Senior Backend Engineer (Data Platform)

Bluefish Labs; New York (NY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Bluefish Labs is seeking a Senior Backend Engineer to take ownership of its data platform infrastructure in New York City. This role entails designing and building production-grade backend services and ensuring the reliability of data delivery.

The ideal candidate will have over 5 years of experience with distributed systems and AWS services. This position follows a hybrid work model with 3 days in the office.

Qualifications

• 5+ years building and operating production backend or data platform systems.
• Strong distributed systems fundamentals.
• Experience with AWS services.
• SQL and NoSQL database experience.
• Robust error handling and idempotency experience.
• Ability to work in Python and TypeScript.

Responsibilities

• Design and build production-grade backend services and pipelines.
• Design and operate data exchange interfaces for external customers.
• Make architectural decisions balancing cost and reliability.
• Build resilient infrastructure with smart retries and failure recovery.
• Participate in end-to-end observability.
• Collaborate cross-functionally to integrate new data sources.
• Set technical direction and raise the bar on code quality.

Skills

Backend development

Distributed systems

Data processing

AWS services (SQS, SNS, Kafka, etc.)

SQL and NoSQL databases

Python

TypeScript

Mentorship

Job description

About the Position

Bluefish AI is looking for a Senior Backend Engineer to own the data platform infrastructure that powers our AI marketing platform; building the systems that reliably move and process data at scale.

This role sits within the Data Engineering team and focuses on building scalable, observable, and resilient systems that handle production data. You will work closely with data engineers and product teams to level up data pipelines and ensure data acquisition scales with the business. This is an exciting opportunity to shape the future of AI‑driven technologies and make meaningful contributions to real‑world applications.

This role is based in our NYC office and follows a hybrid working policy with 3 days in office.

What You’ll Be Doing

• Design and build production‑grade backend services and pipelines that carry data reliably from ingestion to downstream consumers.
• Design and operate data exchange interfaces for external customers: APIs, exports, and delivery mechanisms that meet reliability, security, and contractual requirements.
• Make architectural decisions balancing cost, reliability, and throughput across distributed systems.
• Build resilient infrastructure with smart retries, backoff strategies, dead‑letter handling, and failure recovery.
• Participate in end‑to‑end observability: structured logging, metrics, alerting, and on‑call.
• Collaborate cross‑functionally to integrate new data sources and serve evolving platform needs.
• Set technical direction and raise the bar on code quality, testing, and system design.

Qualifications

• 5+ years building and operating production backend or data platform systems.
• Strong distributed systems fundamentals: event‑driven architectures, async processing, message queues.
• Experience with AWS: SQS/SNS, Kafka, Lambda, ECS, S3, and EventBridge.
• SQL and NoSQL database experience (PostgreSQL, DynamoDB).
• Experience designing systems with robust error handling, idempotency, and reliable delivery guarantees.
• Comfortable working across languages; our stack is Python and TypeScript and you’ll be expected to be productive in both.
• Experience mentoring mid/junior engineers and raising the technical bar across a team.

Nice to Have

• Experience with pipeline orchestrations.
• Background in web scraping or data acquisition at scale.
• Familiarity with vector databases or ML‑adjacent infrastructure (embeddings, inference endpoints).
• Experience with big data, especially with AI/ML platforms and use‑cases.
• Infrastructure as Code experience (Terraform).
• Ability to partner with product, AI, BI, and data science teams as a technical thought partner, translating business and analytical needs into data platform decisions.
• Experience working across time zones in distributed teams, with a track record of defining collaboration rhythms (communication standards, documentation practices, and ceremonies) that keep geographically dispersed teams aligned.