Senior Data Engineer

The Opportunity:

As we scale our data infrastructure to meet the growing complexity and volume of the crypto ecosystem, we’re hiring a Senior Data Engineer to strengthen our Integrations team.

This role focuses on data ingestion, pipeline reliability, and core datasets, ensuring that raw and processed blockchain data flows reliably, predictably, and cleanly into Nansen’s core data platform. The output of this team is high-quality, well-modeled datasets that other engineers, analysts, and product teams can confidently build on.

You’ll work at the intersection of data engineering and software engineering, building robust, long-lived systems that power ingestion pipelines and clean datasets used across the company. You’ll collaborate with a pragmatic, high-performing group of engineers to solve foundational problems with real-world impact.

What You'll Do:

• Design, build, and scale performant data pipelines and infrastructure, primarily using ClickHouse, Python, and dbt.
• Build systems that handle large-scale streaming and batch data, with a strong emphasis on correctness and operational stability.
• Own the end-to-end lifecycle of data pipelines, from raw ingestion to clean, well-defined datasets consumed by downstream teams.
• Improve pipeline observability, data quality checks, and failure handling to ensure predictable operation at scale.
• Collaborate closely with data consumers (data engineers, product engineers, researchers) to define clear dataset contracts and schemas.
• Use AI tools and agents such as Cursor, MCPs, and LLMs to accelerate development, automate repetitive work, and boost quality.
• Bring fresh thinking to the table, staying current with best practices and evolving your toolkit over time.

What We're Looking For:

• Proven experience building and operating production data pipelines that run continuously and reliably.
• Strong data engineering and software engineering fundamentals, with deep experience in Python and SQL.
• Hands-on experience with ClickHouse and dbt in production environments.
• Solid understanding of streaming and batch ingestion patterns, including backfills, reprocessing, and schema evolution.
• Comfortable working across the data infrastructure stack: ingestion, transformation, storage, and exposure to downstream systems.
• Experience designing clean, reusable datasets intended as foundations for other teams.

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