We’re working with a high-growth technology business operating at the intersection of data, trading and digital assets.

The team works with extremely high volumes of real-time financial data, transforming complex streams of information into reliable, commercially valuable datasets used by clients across global markets.

As the data product continues to grow, they’re looking for a Data Quality Engineer to take ownership of the systems that ensure the data is accurate, complete and behaving as expected.

What you’ll be doing:

• Build and maintain dashboards covering live data feeds, historical datasets, venue health, latency, gaps, completeness and data delivery.
• Define meaningful data quality metrics and monitoring standards.
• Validate and reconcile large datasets across multiple sources
• Investigate anomalies such as gaps, stale feeds, duplicates, schema changes and unusual data.
• Build automated checks for live and historical data.
• Develop clear, actionable alerting and monitoring.
• Own internal visibility around client-facing data quality.

What we’re looking for:

• 2+ years’ experience in data quality, data analytics, QA, data operations or data engineering.
• Strong SQL skills and Python experience to analyse datasets, automate checks and investigate issues.
• Experience building dashboards and visualisations for operational or data-quality monitoring.
• Comfortable working with large datasets and comparing information across different sources and systems.
• Comfortable working on the command line and alongside software engineers.

Nice to have:

• Crypto, trading or market microstructure experience.
• Exchange APIs and real-time/streaming systems such as Kafka, Redpanda or NATS.
• ClickHouse, kdb+, Arrow or Parquet.
• Terraform and CI/CD.

If you’re a data-focused engineer who enjoys finding the problems that others miss and wants genuine ownership over a critical data product, I’d love to hear from you.