Senior Data Engineer

The Role

Eve is doubling revenue quarter over quarter, and the platform is being built to keep pace. A medallion architecture is in place on a Terraform-managed Snowflake footprint. Most of what goes on top is still ahead: ingestion and orchestration built for the volume coming, reliability practice, the access and governance model, and the standards every contributor works inside.

The data spans product usage, case and firm data, and every go-to-market system Eve runs on. Analytics engineers and business analysts build models and reporting on top of it, AI agents query it directly, and leadership makes calls on it weekly. When a pipeline fails quietly, all three inherit the mistake.

You'll own the pipelines carrying all of that, end to end, from an ambiguous ask through to production. Moving nightly rebuilds onto incremental patterns. Deciding how a new source gets ingested and what happens when it changes shape. Building the alerting that catches a broken model before anyone downstream does, and being the one who picks it up when it breaks. 

You'll have real say in how the platform gets built. You'll work on our central team and report to the Head of Data Engineering, who reports directly to the CEO. Data is a first-class function at Eve and the fuel to drive our future growth.

What You'll Do

Build and run the platform

• Own ingestion through Fivetran, third-party connectors, and custom extraction where no connector exists
• Own orchestration across dbt platform and GitHub Actions, own materialization strategy and model performance, move critical models off nightly full rebuilds onto incremental patterns, and cut latency where decisions are waiting on stale data
• Build source-schema change detection, so an upstream field change surfaces as an alert before it reaches a report
• Stand up observability: freshness SLAs on critical tables, alerting on failure and drift, and an incident path with clear ownership
• Be a first responder when pipelines break, and drive the fix upstream so the same failure doesn't recur
• Manage pipeline compute cost, and make the tradeoffs between freshness and spend explicit rather than accidental
• Share Snowflake administration: implementing the role-based access model, security and network policies, data masking and PII controls, and storage organization
• Extend the medallion architecture and the Terraform-managed footprint, including full separation of development and production
• Contribute to the foundational modeling layer the Analytics Engineers build on: source-to-staging patterns, conformed dimensions, shared entities, and SCD patterns that make history reliable

Raise the bar

• Build and maintain the development environments and CI that let analysts contribute models safely, and review their contributions so more of the company can build on the foundation
• Build the tooling and setup that gets a new engineer or analyst productive in days, not weeks
• Administer the data tooling stack: access, integrations, and the connective work between systems
• Use AI-assisted development as part of how you work: Claude Code skills, agents, and evals, held to the same review bar as anything else
• Document as you build. If it isn't written down, it isn't done

What We're Looking For

• 5+ years in data engineering, owning production systems other people depended on
• Strong Python and SQL, with production experience across ingestion (Fivetran or similar), orchestration (dbt platform, GitHub Actions, or Airflow), and cloud infrastructure
• Solid Snowflake: access control, warehouse sizing, query performance, and cost management
• Practical dbt: incremental models, testing, macros, and a git-based workflow with CI. Experience building SCD tables from multiple sources
• Comfort in a Terraform-managed environment. Infrastructure changes go through code review here, not the Snowflake console
• You've built reliability practice where none existed: alerting, freshness SLAs, incident response, schema change detection
• Proficiency with AI-assisted development such as Claude Code, including agentic pipeline design and skill-based workflows, and comfort integrating tools via MCP servers
• Able to tell a stakeholder what broke, what it affected, and when it'll be fixed, without the jargon
• Comfort building where the playbook doesn't exist yet

Nice to haves:

• Experience in a regulated or high-sensitivity data environment (legal, healthcare, financial services)
• Experience with streaming or near-real-time ingestion, and knowing when it isn't worth it
• Exposure to Iceberg, Parquet, or unstructured data at scale
• B2B SaaS, especially selling to small and mid-sized businesses or professional services firms