SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Talos is an institutional-grade trading technology platform for the digital asset market, founded in 2018. This Principal Engineer role leads the data quality platform and engineering team, with a mandate to work independently from product engineering and validate all client-facing systems.
You will own data correctness across Talos, ensuring the data returned to clients is always accurate and defects are found before clients discover them. Your responsibilities span three core areas:
**Data Quality & Validation**: Lead AI-first data quality engineering. Validate every client-facing endpoint continuously, including REST and WebSocket APIs, plus cloud delivery systems (S3, Snowflake, Databricks). For streaming data, you'll ensure sequencing, gap detection, snapshot-to-increment consistency, reconnect/replay correctness, and staleness monitoring. Implement adaptive detection using baselines per endpoint and market rather than static thresholds. Apply LLM and agentic tooling to generate checks from API specs and schemas, keeping them current as the product evolves. Design load, performance, and failure-mode tests. When faults are found, drive resolution with the owning team and add preventive checks. Measure and publish endpoint/field coverage, time-to-detect, detected-before-client rate, and false-positive rate.
**Data Governance**: Build and maintain a catalog of data assets and APIs covering lineage and downstream consumers. Own data governance tooling such as OpenMetadata. Develop a deep understanding of assets, their usage, and failure impacts.
**Reference & Security Master Data**: Lead the build-out of the next-generation security master platform, then operate it day-to-day. Manage new listings, symbol mapping across venues, and increment/contract specs. Design safe update processes with validation and rollback capabilities.
**Expansion**: Over time, extend data quality coverage beyond client APIs to trade signals, pre-trade analytics, client post-trade analysis, and PMS analytics.
The team is small, globally distributed with hubs in New York, London, Singapore, and Cyprus, operating in a hybrid-friendly environment.
**Requirements**:
- Track record leading data QA or data quality teams, preferably on market data or high-throughput, real-time data
- Deep understanding of streaming and API behavior: throughput, WebSocket semantics, ordering, recovery, and failure modes
- Experience inventorying and testing a large API and data surface end-to-end
- Hands-on experience with data governance: cataloging, lineage, ownership
- Experience with performance and load testing of distributed systems
- Strong engineering background designing platforms (not scripts), building on shared infrastructure
- Practical experience applying LLM and agentic tooling to engineering problems
- Familiarity with Flink, Doris, Dagster, Spark, and OpenMetadata (or comparable tools like Apache Atlas)
