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Senior Data Engineer

Chainlink Labs

About Chainlink Labs
Chainlink Labs is one of the primary contributing developers of Chainlink, the standard for onchain finance enabling high-quality data, secure cross-chain interoperability, and compliant connectivity across global markets. Chainlink powers leading tokenized assets, financial institutions, market infrastructures, and decentralized applications by providing secure access to real-world data, cross-chain messaging, and blockchain connectivity across any network. The Chainlink standard has enabled tens of trillions of dollars in transaction value and is helping unify global markets onchain. Many of the world’s largest financial services institutions have also adopted Chainlink’s standards and infrastructure, including Swift, Euroclear, Mastercard, Fidelity International, UBS, S&P Dow Jones Indices, FTSE Russell, WisdomTree, ANZ, Aave, Polymarket, Lido, and many others.About Chainlink Labs
Chainlink Labs is a world-class team of developers, researchers, and capital markets experts, and has ranked among Fortune's Best Workplaces in Technology, Fortune's Best Medium Workplace, and the Top 100 Global Most Loved Workplaces. Learn more at chain.Link or chainlinklabs.Com.About Chainlink
Chainlink is the industry-standard oracle platform bringing the capital markets onchain and powering the majority of decentralized finance (DeFi). The Chainlink stack provides the essential data, interoperability, compliance, and privacy standards needed to power advanced blockchain use cases for institutional tokenized assets, lending, payments, stablecoins, and more. Since inventing decentralized oracle networks, Chainlink has enabled tens of trillions in transaction value and now secures the vast majority of DeFi.Many of the world’s largest financial services institutions have also adopted Chainlink’s standards and infrastructure, including Swift, Euroclear, Mastercard, Fidelity International, UBS, S&P Dow Jones Indices, FTSE Russell, WisdomTree, ANZ, and top protocols such as Aave, Lido, GMX and many others. Chainlink leverages a novel fee model where offchain and onchain revenue from enterprise adoption is converted to LINK tokens and stored in a strategic Chainlink Reserve. Learn more at chain.Link.About the Role
This role exists to ensure the reliability, accuracy, and observability of Chainlink’s data systems as the network scales across assets and use cases. It focuses on building robust data pipelines and models that power benchmarking, monitoring, and downstream data access. The role enables faster product development and unlocks quant and data science workflows through high-quality, accessible data.Your Impact
Deliver data pipelines that achieve 99.99% ingestion uptime across critical datasetsImprove benchmark accuracy by building resilient, scalable data modeling and processing systemsEstablish end-to-end observability, ensuring data freshness, completeness, and accuracy are continuously validatedRoute actionable alerts to the appropriate owners, reducing noise and accelerating issue resolutionEnable rapid delivery of new data methodologies to support emerging products and asset coverageProvide clean, well-structured datasets that accelerate modeling, analytics, and decision‑making across teamsRequirements
Demonstrated experience building scalable data models and pipelines for analytics using SQL and PythonExperience working with blockchain or traditional financial market data, with a clear understanding of one domainProven ability to define and implement metrics for evaluating complex systems, including data quality and system performanceBuilt and maintained data pipelines handling large-scale, time‑series or asynchronous datasetsStrong understanding of data modeling, partitioning, and performance optimization in analytical systemsExperience designing and implementing data quality checks covering freshness, completeness, and accuracyPreferred Requirements
Experience working with real‑time or streaming data systems such as Flink, Beam, or similar frameworksFamiliarity with order books, trade data, and aggregation methodologies in financial systemsUnderstanding of decentralized exchanges and approaches to sourcing and structuring onchain dataBackground in quantitative statistics or applied statistical analysisExperience working in investment, trading, or market data environmentsDemonstrated ability to work with noisy, multi‑source datasets and derive reliable analytical outputsChainlink Labs is a remote‑first organization and employment eligibility is contingent upon residing in an approved hiring jurisdiction. Team members may work outside their primary country of legal residence for a maximum of 90 days per calendar year. Please discuss your current location, legal residence, and work authorization with your recruiter during your initial conversation. Unless otherwise stated for a specific role, we require working hours that overlap with Eastern Time (ET).We carefully review all applications and aim to provide a response to every candidate within two weeks after the job posting closes.
The closing date is listed on the job advert, so we encourage you to take the time to thoughtfully prepare your application.
We want to fully consider your experience and skills, and you will hear from us regarding the status of your application shortly after the closing date.Commitment to Equal Opportunity
Chainlink Labs is an equal opportunity employer. All qualified applicants will receive equal consideration for employment in compliance with applicable laws, regulations, or ordinances. If you need assistance or accommodation due to a disability or special need when applying for a role or in our recruitment process, please contact us via this form.Global Data Privacy Notice for Job Candidates and Applicants
Information collected and processed as part of your Chainlink Labs Careers profile, and any job applications you choose to submit, is subject to our Recruiting Privacy Policy. By submitting your application, you are agreeing to our use and processing of your data as required.
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Vacancy posted 38 minutes ago
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