Senior Data Engineer - Scalable Pipelines & Observability

Framework Ventures; Charlotte (AR); Remote; USD 130,000 - 180,000; Full time

Job summary

Chainlink Labs is seeking a data-focused engineer to ensure reliable, high-quality data delivery across assets and use cases. You will build robust pipelines and models that power benchmarking, monitoring, and downstream data access, enabling faster product development and cross-team analytics.

Ideal candidates have strong SQL/Python skills, experience with time-series data, and a track record of implementing data quality checks.

Qualifications

• 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.

Responsibilities

• Deliver data pipelines that achieve high uptime across critical datasets.
• Improve benchmark accuracy by building scalable data modeling and processing systems.
• Establish end-to-end observability, ensuring data freshness, completeness, and accuracy.
• Route actionable alerts to owners to reduce noise and speed up resolution.
• Enable rapid delivery of new data methodologies to support products and asset coverage.
• Provide clean, well-structured datasets that accelerate modeling and analytics.

Skills

SQL

Python

Data modeling

Time-series

Data quality

Metrics

Tools

Flink

Beam

Job description

Chainlink Labs is seeking a data-focused engineer to ensure reliable, high-quality data delivery across assets and use cases. You will build robust pipelines and models that power benchmarking, monitoring, and downstream data access, enabling faster product development and cross-team analytics.

Ideal candidates have strong SQL/Python skills, experience with time-series data, and a track record of implementing data quality checks.