Python Analytics Engineer - Global Digital Asset Firm
Our client, a leading global digital asset trading organisation, is seeking a Python Analytics Engineer to join its quantitative technology team. This role sits at the intersection of large‑scale data engineering, AI platform development, and quantitative trading, supporting real‑time decision‑making across multiple trading desks. The successful candidate will help build modern analytics infrastructure, enhance data quality, and contribute to AI‑driven tooling used in fast‑moving crypto markets.
Data & Analytics Engineering
- Build and maintain high‑throughput data pipelines for both streaming and batch workloads using modern messaging and ETL technologies such as Kafka and Flink.
- Design analytical data models to support time‑series analysis, trading metrics, and performance monitoring.
- Manage and optimize analytical databases (e.g., ClickHouse, MongoDB, Snowflake, BigQuery) with cost‑efficient and scalable architectural patterns.
- Implement reliable backfill, reconciliation logic, and data quality monitoring for mission‑critical datasets.
- Build internal AI platforms to serve multiple trading teams and accelerate model and analytics development.
- Develop vector‑based retrieval systems utilizing advanced indexing approaches such as HNSW and hybrid search.
- Create evaluation tooling for retrieval quality (e.g., Recall@K, nDCG, MRR) and support robust RAG‑based workflows.
- Develop reusable AI utilities including standardized prompt management, RAG components, and agent frameworks using modern libraries (e.g., LangGraph, MCP, A2A).
- Partner with trading, quant, and data teams to deliver scalable systems that support analytics, research, and real‑time trading operations.
- Ensure system reliability, transparency, and performance across data and AI workloads.
Requirements
- 5+ years of hands‑on software engineering experience, with strong proficiency in Python and Java in production environments.
- Strong fundamentals in system design, algorithms, data structures, data integrity, and performance optimisation.
- Experience working with Linux, GitHub workflows, CI/CD practices, and cloud-native deployments.
- Proven experience with AWS and Kubernetes for large‑scale distributed workloads.
- Comfortable working with large, complex datasets in trading or financial domains.
- Solid SQL knowledge, including window functions, query tuning, and real‑time database synchronization.
- Experience with workflow orchestration tools such as Airflow, Jenkins, or similar.
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