Market Data Quality Engineer – Python Pipelines & SQL

Imc; Sydney; On-site; AUD 120,000 - 160,000; Full time

Job summary

IMC seeks a Market Data Quality Analyst to own reference data, corporate actions, and alternative data from exchanges and vendors. You will build end-to-end Python pipelines for ingestion and publication, and apply SQL, Pandas, and Polars to validate and model large datasets.

You will work with researchers, traders, and engineers to define data requirements, monitor quality, and resolve anomalies, ensuring datasets are trading-ready and well-documented.

Qualifications

• 5+ years in a data-focused role, preferably in trading/finance.
• Strong Python including data processing frameworks (Pandas/Polars).
• Advanced SQL for analyzing large datasets.
• Interest in how exchanges work: instrument lifecycles, session structures, calendars.
• Detail-focused to document data edge cases and quality issues.
• Strong cross-functional communication with researchers, traders, and engineers.

Responsibilities

• Acquire, clean, and maintain reference data and alternative data sources.
• Design and own automated Python pipelines for ingestion, transformation, publication.
• Analyze datasets using SQL, Pandas, and Polars to model and investigate issues.
• Monitor data quality, resolve anomalies, and develop automated tests.
• Collaborate with engineers to enhance data storage and retrieval for research.
• Turn data needs into well-defined, maintained datasets for users.

Skills

Python

Pandas/Polars

SQL

Data quality

Cross-functional collaboration

Data ingestion/validation

Tools

Spark

Dask

Kubernetes

Job description

IMC seeks a Market Data Quality Analyst to own reference data, corporate actions, and alternative data from exchanges and vendors. You will build end-to-end Python pipelines for ingestion and publication, and apply SQL, Pandas, and Polars to validate and model large datasets.

You will work with researchers, traders, and engineers to define data requirements, monitor quality, and resolve anomalies, ensuring datasets are trading-ready and well-documented.