Senior Data Engineer [Remote]

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](

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](

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.

• Deliver data pipelines that achieve 99.99% ingestion uptime across critical datasets
• Improve benchmark accuracy by building resilient, scalable data modeling and processing systems
• Establish end-to-end observability, ensuring data freshness, completeness, and accuracy are continuously validated
• Route actionable alerts to the appropriate owners, reducing noise and accelerating issue resolution
• Enable rapid delivery of new data methodologies to support emerging products and asset coverage
• Provide clean, well-structured datasets that accelerate modeling, analytics, and decision-making across teams

• Demonstrated experience building scalable data models and pipelines for analytics using SQL and Python
• Experience working with blockchain or traditional financial market data, with a clear understanding of one domain
• Proven ability to define and implement metrics for evaluating complex systems, including data quality and system performance
• Built and maintained data pipelines handling large-scale, time-series or asynchronous datasets
• Strong understanding of data modeling, partitioning, and performance optimization in analytical systems
• Experience designing and implementing data quality checks covering freshness, completeness, and accuracy

• Experience working with real-time or streaming data systems such as Flink, Beam, or similar frameworks
• Familiarity with order books, trade data, and aggregation methodologies in financial systems
• Understanding of decentralized exchanges and approaches to sourcing and structuring onchain data
• Background in quantitative statistics or applied statistical analysis
• Experience working in investment, trading, or market data environments
• Demonstrated ability to work with noisy, multi-source datasets and derive reliable analytical outputs

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](

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