SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 185,000 - 245,000 / annual
Eve is a legal technology platform serving plaintiff law firms with AI-native case management and workflow automation. 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. The team includes engineers from Scale, Meta, Airbnb, Cruise, Square, Rubrik, and Lyft, and the company collaborates directly with OpenAI and Anthropic on AI workflows.
As Senior Data Engineer, you will own the end-to-end data platform that powers product analytics, case/firm data, and all go-to-market systems. Eve's data architecture uses a medallion pattern on Terraform-managed Snowflake. You will be responsible for building and maintaining production pipelines that serve analytics engineers, business analysts, AI agents, and executive decision-making.
Key responsibilities include:
**Build and run the platform:**
- Own data ingestion via Fivetran, third-party connectors, and custom extraction
- Manage orchestration using dbt and GitHub Actions; optimize materialization strategy and move critical models from nightly full rebuilds to incremental patterns
- Build source-schema change detection to surface upstream field changes as alerts before they reach reports
- Implement observability: freshness SLAs, failure/drift alerting, and incident response procedures
- Be first responder for pipeline failures and drive upstream fixes to prevent recurrence
- Manage pipeline compute costs and make freshness/spend tradeoffs explicit
- Share Snowflake administration: role-based access control, security/network policies, data masking, PII controls, storage organization
- Extend medallion architecture and Terraform-managed footprint with full dev/prod separation
- Contribute to foundational modeling layer: source-to-staging patterns, conformed dimensions, shared entities, SCD patterns
**Raise the bar:**
- Build development environments and CI/CD for safe analyst contributions and code review
- Create tooling and setup to onboard new engineers/analysts in days, not weeks
- Administer data tooling stack: access, integrations, system connectors
- Use AI-assisted development (Claude Code, agents, evals) as part of your workflow
- Document as you build; treat documentation as a first-class deliverable
**Requirements:**
- 5+ years in data engineering, owning production systems others depend on
- Strong Python and SQL with production experience in ingestion (Fivetran or similar), orchestration (dbt, GitHub Actions, or Airflow), and cloud infrastructure
- Solid Snowflake expertise: access control, warehouse sizing, query performance, cost management
- Practical dbt experience: incremental models, testing, macros, git-based CI workflow, SCD table design from multiple sources
- Comfort in Terraform-managed environments; infrastructure changes via code review, not console
- Built reliability practice from scratch: alerting, freshness SLAs, incident response, schema change detection
- Proficiency with AI-assisted development (Claude Code, agentic pipeline design, skill-based workflows, MCP server integration)
- Ability to communicate clearly to stakeholders: what broke, what it affected, when it will be fixed, without jargon
- Comfort building where playbooks don't exist yet
**Nice to haves:**
- Experience in regulated or high-sensitivity data environments (legal, healthcare, financial services)
- Streaming or near-real-time ingestion experience; judgment about when it's worth it
- Exposure to Iceberg, Parquet, or unstructured data at scale
- B2B SaaS background, especially selling to SMBs or professional services firms
