Senior Data Engineer

The role

You will work across the full data lifecycle, from understanding a source system to designing the contracts, pipelines, storage, and operational practices that make the resulting product trustworthy. This is a hands-on role for someone who enjoys both a good technical design and the responsibility of running it in production.

What you will work on

• Lead the design of batch and streaming data systems for new and existing products.
• Work with founders and domain experts to turn ambiguous requirements into useful data models and interfaces.
• Build dependable ingestion, transformation, and serving workflows with clear ownership and failure handling.
• Set practical standards for data quality, testing, lineage, observability, documentation, and incident response.
• Partner with infrastructure engineers on storage, compute, security, deployment, and cost decisions.
• Investigate difficult production issues and improve the system so the same class of issue becomes less likely.
• Review designs and code, mentor engineers, and help make complex systems easier to operate.

What we are looking for

• At least five years of experience building and operating production data systems.
• Strong SQL and Python fundamentals, with the ability to choose appropriate tools rather than follow fashion.
• Experience with data modeling, orchestration, warehouses or lakehouses, and one or more cloud platforms.
• A clear understanding of reliability, observability, access control, and the operational cost of data systems.
• Confidence working directly with stakeholders and explaining tradeoffs in plain language.
• A habit of writing down decisions and leaving systems clearer than you found them.

Helpful, but not required

• Experience with streaming systems, event-driven architectures, or high-volume financial data.
• Experience setting up platform conventions used by multiple engineering teams.
• Interest in infrastructure as code, developer tooling, or open source data tooling.

What success looks like

• Teams can understand where important data comes from, how it changes, and whether it is safe to use.
• New pipelines are delivered with sensible tests, monitoring, documentation, and clear ownership.
• The data platform becomes easier to operate and more predictable as its workload grows.