Lead the data quality engineering team at Talos, ensuring data correctness across client-facing endpoints in a hybrid-friendly environment.
Posted by employer 1 day ago
First seen on Joblaze 1 hour ago
Last verified on the company career page 1 hour ago
Skills & Technologies
What you'll build
Must have
Nice to have
AI in the day-to-day
Apply LLM and agentic tooling to generate checks from API specs and schemas.
Not disclosed in this posting: compensation, years of experience, visa sponsorship.
Benefits
Joblaze summary
In the role of Principal Engineer for the Data Quality Platform at Talos, the individual will oversee the integrity of data across various systems, ensuring that client-facing endpoints deliver accurate information. Key skills include expertise in streaming data, API behavior, and data governance, with a focus on using advanced tools like LLM for quality assurance. This position is suited for a senior engineer with a strong background in data quality and performance testing, particularly in high-throughput environments. Talos fosters a collaborative culture within a small, experienced team dedicated to enhancing the digital asset market.
Joblaze insights
Quick facts
From the original posting
You'll own data correctness across Talos. You'll 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.
Our goal: the data we return to clients is always correct, and we find defects before a client does.
You'll join a small team with the mandate to work independently from product engineering and challenge it. Engineering teams make assumptions about how their systems behave. Your job is to test those assumptions and prove or disprove them with evidence.
Lead AI-first data quality engineering
Validate every client-facing endpoint continuously. The focus is our REST and WebSocket APIs. Cloud delivery (S3, Snowflake, Databricks) is also in scope. Assert correctness against independent sources of truth, across every environment and client entitlement.
Cover streaming as well as request/response. For WebSocket, that means:
sequencing and gap detection
snapshot-to-increment consistency
reconnect and replay correctness
staleness of live subscriptions
agreement between the streamed view and the REST view of the same state
Use adaptive detection, not static thresholds. Learn baselines per endpoint and per market, and let them adjust as "normal" changes. Hand-set limits get noisy, go stale, and teach people to ignore alerts.
Apply LLM and agentic tooling to generate checks from API specs and schemas, keep them current as the product changes, and triage alerts before a human sees them. Know when a deterministic rule, invariant or cross-source reconciliation is the better tool.
Test resilience and load. Design load, performance and failure-mode tests.
When you find a fault, raise it with the owning team with enough evidence for them to act. Drive it to resolution, then add a check that would have caught it earlier.
Measure and publish endpoint and field coverage, time to detect, detected-before-client rate and false-positive rate.
Run data governance
Build and maintain a catalog of our data assets and APIs, covering lineage and downstream consumers. Maintain an understanding of assets, how they're used and what breaks when they change.
Own data governance tooling such as OpenMetadata.
Own reference and security master data
Lead the build-out of the next-generation security master platform, then run it day to day: new listings, symbol mapping across venues, and increment and contract specs.
Design processes for safe updates to security entries, with validation and rollback.
Expand coverage across the firm
Over time, extend data quality coverage beyond client APIs to:
trade signals
pre-trade analytics
client post-trade analysis
PMS analytics
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. You design platforms, not scripts on a cron job, and you build on shared infrastructure rather than creating one-off tooling.
Practical experience applying LLM and agentic tooling to engineering problems.
Our stack: Flink, Doris, Dagster and Spark, plus OpenMetadata (or an equivalent such as Apache Atlas). Experience with comparable tools is just as relevant.
Spotlight Article: Talos Snapshot by DRW
Culture: Award-winning, Forbes-listed, low-attrition team
Retirement plan contributions
Yearly discretionary cash bonus
Employee stock options plan
Medical, dental, and vision insurance
Global offsites and team outings
Donation matching up to $100 USD annually
Check out other open positions listed on our website!
Talos is proud to be an Equal Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Talos is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at careers@talos.com.
© Talos Trading, careers@talos.com
© Talos Trading, careers@talos.com
Standard company text repeated across Talos's postings is omitted here.