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Salary: USD 220,000 - 300,000 / annual
Eve is a legal technology platform serving plaintiff law firms, built on AI-native workflows that help firms handle more cases and recover more for clients. The company has achieved product-market fit with 1000+ law firms, raised $160M from top-tier investors (Spark Capital, A16z, Menlo Ventures, Lightspeed), and is growing 2X revenue quarter over quarter.
As Staff Data Engineer, you will set technical direction for Eve's data platform and own the problems where the right answer isn't obvious yet. You'll work on the central data team reporting to the Head of Data Engineering, who reports directly to the CEO. Data is a first-class function at Eve.
The platform currently runs on a medallion architecture with Terraform-managed Snowflake infrastructure. Most foundational work remains ahead: access and governance models, ingestion and orchestration built for scale, reliability practices, and contributor standards. The data spans product usage, case and firm data, and all go-to-market systems. Analytics engineers and business analysts build models and reporting on top; increasingly, AI agents query the warehouse directly, raising the bar on data quality, access controls, and freshness guarantees.
Key responsibilities include:
**Build and run the platform:**
- Own the role-based access model: role hierarchy, least-privilege grants across medallion layers, scoped access for service accounts, BI tools, and AI agents
- Manage grants as code and run access review cycles as audit evidence
- Own Snowflake administration: security and network policies, data masking, PII controls, storage organization, compute cost, and retention
- Extend the medallion architecture and Terraform-managed footprint; own state, module design, and environment promotion with full dev/prod separation
- Own ingestion through Fivetran, third-party connectors, and custom extraction; define data governance for what lands in the warehouse
- Own orchestration across dbt platform and GitHub Actions; manage materialization strategy, model performance, and move critical models from nightly full rebuilds to incremental patterns
- Build source-schema change detection to surface upstream field changes as alerts before reaching reports
- Stand up observability: freshness SLAs on critical tables, alerting on failure and drift, and clear incident response ownership
- Contribute to foundational modeling: source-to-staging patterns, conformed dimensions, shared entities, and SCD patterns
**Set the direction:**
- Define dbt project architecture, git-based development workflow, CI, and testing standards; own macro and package libraries and isolated development environments
- Partner on data retention and customer data handling policy; implement technical controls
- Establish how the team uses AI-assisted development (Claude Code, agents, evals) as part of the workflow
- Build tooling and setup to get new engineers and analysts productive in days, not weeks
- Document as you build; if it isn't written down, it isn't done
- Mentor engineers and analysts; set the technical bar for data engineering at Eve
**Requirements:**
- 8+ years in data engineering, including time at staff or senior IC level setting technical direction
- Deep Snowflake administration experience, especially designing role-based access models from scratch: role hierarchy, least-privilege grants, scoping access for service accounts and tools, 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 expertise: modeling patterns, macros, incremental models, testing, slim or state-based CI, and SCD tables from multiple sources
- Experience building dbt developer experience for contributors outside core engineering: project structure, guardrails, and CI for mixed skill levels
- Experience with layered warehouse architecture (medallion or equivalent) and infrastructure as code with Terraform, including state and environment promotion
- Track record building access and governance layers that passed audit requirements
- Track record building reliability practice from zero: freshness SLAs, alerting, incident response, schema change detection
- Proficiency with AI-assisted development (Claude Code), including agentic pipeline design, skill-based workflows, and MCP server integration
- Strong communication, mentoring habit, and comfort building where playbooks don't exist
**Nice to haves:**
- Experience in regulated or high-sensitivity data environments (legal, healthcare, financial services)
- Experience supporting ML or GenAI workloads: feature stores, unstructured data, Snowflake Cortex
- B2B SaaS experience, especially selling to SMBs or professional services firms
