MarketAxess is looking to digitally transform one of the world’s largest financial markets, enabling the shift from analog, phone-based trading to a fully electronic marketplace, and improve transparency, efficiency, and competition in the marketplace.
Requirements
- 5+ years in data-engineering or ML-engineering roles.
- Expert in Python (pandas, NumPy, PySpark) and Java (or JVM-based streaming frameworks).
- Strong SQL skills: complex queries, window functions, performance tuning.
- Hands-on experience with AWS (S3, EMR/Spark, Redshift/Athena/Glue).
- Proficiency in designing and maintaining big-data workflows (Spark, Flink, or similar).
- Production experience building streaming pipelines (Apache Flink, Spark Structured Streaming, Kafka Streams, or equivalent).
- Exposure to ML lifecycle: feature engineering, model packaging, evaluation, and deployment (e.g., SageMaker or equivalent).
Responsibilities
- Build scalable ingestion pipelines in Python, Java, and SQL.
- Use Apache Spark or Flink (or equivalent) to process high-volume data streams with sub-second SLAs.
- Refactor legacy ETL (often provided as R scripts) into maintainable Python/Java code.
- Read, interpret, and reimplement R-based statistical or quantitative models (e.g., regressions, risk metrics) into production-grade Python (NumPy/Pandas/SciPy) or Java.
- Design and maintain AWS-based data platforms (S3, EMR/Spark, Redshift/Athena/Glue).
- Develop real-time feature pipelines consuming market or trade feeds (e.g., Kafka).
- Implement automated build/test/deploy pipelines (Jenkins, GitLab CI/CD, or equivalent).
Other
- B.S or M.S degree in Computer Science, Engineering, Data Science, or a quantitative discipline.
- Excellent interpersonal skills; able to convey technical concepts to diverse audiences.
- Demonstrated success in an Agile/Scrum environment.
- Familiarity with Fixed Income markets (bond analytics, yield curves, credit spreads).
- Hybrid Environment: Our employees enjoy a mix of working in the office and from home.
