Full Stack Engineer – Data Engineering Focus
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Job Description
Full Stack Engineer focusing on data engineering at GNO Partners. Building scalable data pipelines and contributing to AI insights layer for Amazon sellers.
Responsibilities:
- Design and build the data pipelines that pull from Amazon SP-API and Ads API into our Postgres warehouse — handling rate limits, retries, schema evolution, and the messy realities of vendor APIs.
- Architect ingestion, transformation, and aggregation layers that power our reporting tools.
- Build new report tools end-to-end alongside the rest of the engineering team — you're not only on pipelines.
- Help us scale our data layer as we move from per-client manual uploads to automated, multi-tenant data flow.
- Contribute to our AI insights layer — feeding clean, structured data into LLM-powered analysis.
Requirements:
- 3–5 years of full-stack experience, with a demonstrable track record of building data pipelines in production.
- Strong with TypeScript, Node.js, NestJS, and React.
- Deep comfort with Postgres / Supabase — partitioning, indexing, query optimization, handling large datasets.
- Hands-on experience with AWS : S3, Lambda, SNS, SQS, EC2.
- Bonus for orchestration tools (Step Functions, EventBridge, Airflow, Temporal, etc.).
- Experience integrating with third-party APIs at scale — pagination, rate limiting, backfills, incremental sync.
- Basic familiarity with AI agentic systems — you've worked with or explored LLMs, tool-use, or agent frameworks.
- Pragmatic and product-aware — you understand pipelines exist to serve user-facing features, not for their own sake.
- Nice to Have: Direct experience with Amazon SP-API and/or Ads API. Background with ETL frameworks, streaming systems (Kafka, Kinesis), or workflow engines (Temporal, Airflow). Experience with data quality tooling, observability, or pipeline monitoring.
Benefits:
- You'll own the data backbone of a platform that's actively scaling.
- High-leverage work — every pipeline you build directly enables new product surface area.
- Clear path to work across pipelines, product, and AI as the platform evolves.
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