Principal Engineer, Data Quality Platform

Talos provides institutional-grade trading technology for the global digital asset market. The Principal Engineer, Data Quality Platform will own data correctness across Talos, lead the data quality engineering team, run BAU data operations, and own reference and security master data, along with the pipelines and platform that deliver it. The goal is that the data returned to clients is always correct and defects are found before a client does. The role works independently from product engineering to challenge its assumptions and prove or disprove them with evidence.

Responsibilities

Validate every client-facing endpoint continuously, focusing on REST and WebSocket APIs and cloud delivery (S3, Snowflake, Databricks), asserting correctness against independent sources of truth across every environment and client entitlement; Cover streaming behaviour, including sequencing and gap detection, snapshot-to-increment consistency, reconnect and replay correctness, staleness of live subscriptions, and agreement between streamed and REST views; Use adaptive detection with learned baselines per endpoint and per market instead of static thresholds; Apply LLM and agentic tooling to generate checks from API specs and schemas, keep them current, and triage alerts; Test resilience and load, designing load, performance and failure-mode tests; When faults are found, raise them with the owning team with evidence, drive resolution, and add a check that would have caught them earlier; Measure and publish endpoint and field coverage, time to detect, detected-before-client rate and false-positive rate; Build and maintain a catalog of data assets and APIs covering lineage and downstream consumers, and own data governance tooling such as OpenMetadata; Lead the build-out of the next-generation security master platform and run it day to day, including new listings, symbol mapping across venues, and increment and contract specs; Design processes for safe updates to security entries, with validation and rollback; Extend data quality coverage beyond client APIs to trade signals, pre-trade analytics, client post-trade analysis and PMS analytics; Lead the data quality engineering team, run BAU data operations, and own reference and security master data along with the pipelines and platform that deliver it

Requirements

A track record of leading data QA or data quality teams, preferably on market data or other high-throughput, real-time data; A deep understanding of streaming and API behaviour: 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; A strong engineering background, designing platforms and building on shared infrastructure; Practical experience applying LLM and agentic tooling to engineering problems; Experience with the stack (Flink, Doris, Dagster, Spark, OpenMetadata or an equivalent such as Apache Atlas), or comparable tools

Benefits

Retirement plan contributions; Unlimited vacation plus a PTO bonus of $2000 USD for taking 15 days of vacation, with 5 consecutive; Yearly discretionary cash bonus; Employee stock options plan; Medical, dental, and vision insurance; Paid parental leave for 20 weeks after 1 year of service; Global offsites and team outings; One-time stipend towards at-home office set-up; Wellness credit of $50 USD accrued monthly; Lunch credit of $25 USD for in-office days; Donation matching up to $100 USD annually; In-office perks including catered lunches on Tuesdays, snacks, and drinks