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Talos is an institutional-grade trading technology platform for the digital asset market, founded in 2018. This Principal Engineer role owns data correctness across the entire Talos platform and leads the data quality engineering team.
You will lead a small, independent team with the mandate to challenge product engineering assumptions and validate them with evidence. Your responsibilities span three core areas:
**Data Quality & Validation**: Lead AI-first data quality engineering across all client-facing endpoints (REST APIs, WebSocket streams, and cloud delivery via S3, Snowflake, Databricks). Validate correctness against independent sources of truth across every environment and client entitlement. For WebSocket specifically, you'll ensure sequencing, gap detection, snapshot-to-increment consistency, reconnect/replay correctness, staleness detection, and agreement between streamed and REST views. Implement adaptive detection using LLM and agentic tooling to generate checks from API specs and schemas, keeping them current as the product evolves. Triage alerts before human escalation. Design and execute load, performance, and failure-mode tests. When faults are discovered, drive resolution with the owning team and add preventative 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. Maintain understanding of how assets are used and what breaks when they change.
**Master Data & Security**: Lead the build-out of the next-generation security master platform, then run it operationally. Own new listings, symbol mapping across venues, increment and contract specs. Design safe update processes with validation and rollback. Over time, expand data quality coverage to trade signals, pre-trade analytics, client post-trade analysis, and PMS analytics.
Talos operates globally distributed hubs in New York, London, Singapore, and Cyprus with a hybrid-friendly culture emphasizing kindness, respect, and diverse perspectives.
**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; you design platforms (not scripts on cron jobs) and build on shared infrastructure
- Practical experience applying LLM and agentic tooling to engineering problems
- Familiarity with stack: Flink, Doris, Dagster, Spark, OpenMetadata (or equivalents like Apache Atlas)
