Staff Data Engineer

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: the access and governance model, ingestion and orchestration built for the volume coming, reliability practice, 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, and increasingly AI agents query it directly. That last part raises the bar on everything underneath: an agent with broad access is a different kind of risk than a person with broad access, and a stale model is a different kind of problem when something automated is reading it rather than a human who would notice the number looked wrong.

As the Staff engineer, you set technical direction and take on the problems where the right answer isn't obvious yet. What the warehouse looks like at ten times the volume. How access works for a company holding client and case data. Which tools to buy and which to build.

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.

Build and run the platform

Own the role-based access model: role hierarchy, least-privilege grants across the medallion layers, and scoped access for service accounts, BI tools, and AI agents rather than broad inherited access

Manage grants as code and run the access review cycle, so the process itself is the audit evidence

Own Snowflake administration: security and network policies, data masking and PII controls, storage organization, compute cost, and retention

Extend the medallion architecture and the Terraform-managed footprint. Own state, module design, and environment promotion, including full separation of development and production

Own ingestion through Fivetran, third-party connectors, and custom extraction where no connector exists, including a defined review of what data is allowed to land

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

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

Set the direction

Define the dbt project architecture and the git-based development workflow, CI, and testing standards every model passes through, including the macro and package libraries and the isolated development environments that let analysts contribute safely

Partner on data retention and customer data handling policy, and implement the technical controls behind it

Establish how the team uses AI-assisted development: Claude Code skills, agents, and evals as part of the workflow, held to the same review bar as anything else

Build the tooling and setup that gets a new engineer or analyst productive in days, not weeks

Document as you build. If it isn't written down, it isn't done

Mentor engineers and analysts, and set the technical bar for how data engineering gets done at Eve

What We're Looking For

8+ years in data engineering, including time at a staff or senior IC level setting technical direction others followed

Deep Snowflake administration, especially designing a role-based access model from scratch: role hierarchy, least-privilege grants, and scoping access for service accounts and tools. Plus masking, PII controls, and cost management

Strong Python and SQL, with production experience across ingestion (Fivetran or similar), orchestration (dbt platform, GitHub Actions, or Airflow), and cloud infrastructure

Advanced dbt: modeling patterns, macros, incremental models, testing, and slim or state-based CI. Experience building SCD tables from multiple sources

Experience building the dbt developer experience for contributors outside a core engineering team: project structure, guardrails, and CI that lets people with mixed skill levels extend a codebase safely

Experience with layered warehouse architecture (medallion or equivalent) and managing infrastructure as code with Terraform, including state and environment promotion

You've built the access and governance layer somewhere that had to pass an audit

Track record building reliability practice from zero: freshness SLAs, alerting, 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

Strong communication, a habit of mentoring, and 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 supporting ML or GenAI workloads: feature stores, unstructured data, Snowflake Cortex

B2B SaaS, especially selling to small and mid-sized businesses or professional services firms