The role involves building and integrating data pipelines, enhancing feature stores for real-time ML applications, and improving data monitoring for FinCrime contexts.
🌔 About the Opportunity
Our engineering discipline builds the technology that enables MoonPay to learn quickly and scale easily. We organize in small cross-functional squads of 4-6 engineers and an embedded Product Manager and Product Data Analyst. We currently have squads across Crypto / NFT / Payments / FinCrime / KYC / Core Product and others. For this role, the initial focus will be on working on our FinCrime products, while mastering other product areas to then spearhead ML and AI adoption in the company.
🚀 What you will do
- Build and integrate data pipelines for ingesting data, processing and serving features in real-time in a high throughput/low latency environment
- Support multiple data models that serve critical data for FinCrime products (ML models, risk engine, etc.)
- Own our Feature Store development, expanding our feature engineering capabilities for stateless and stateful data for both offline and online serving
- Enhance our monitoring capabilities, adding new data alerts for drift, anomalies, latency, etc
- Analyze large datasets using SQL, Apache Beam and Polars to surface features
- Help build AI-powered automation tools or pipelines and propose improvements across the company
- Maintain and improve our existing codebase, expanding our internal Feature Store and ML libraries and pipelines
💻 What you will be working with/on
- Apache Beam
- Dataflow
- BigTable
- Redis
- BigQuery
- Python, Polars, Pandas and Numpy
- ML feature engineering for fraud prevention
- FastAPI, Docker, Kubernetes
- Kubeflow and Airflow
- Vertex AI
- Pydantic
- DataDog
- Github
🧑🚀 About You
- Experienced in Data Engineering at leading Fintech startups or fast growing tech companies
- Curious about Machine Learning and with strong fundamentals about data modelling for feature generation
- Experienced with some of our tech stack, or are confident you can cross train and up skill quickly
- Understand data structures, pipelines and big data processing for real-time consumption
- Real world experience working with feature stores (in-house or vendor based e.g. Tecton)
- Experienced with Cloud Native applications such as Google Cloud or similar e.g. AWS, Azure
Top Skills
Airflow
Apache Beam
BigQuery
Bigtable
Datadog
Dataflow
Docker
Fastapi
Git
Kubeflow
Kubernetes
Numpy
Pandas
Polars
Pydantic
Python
Redis
Vertex Ai
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