Staff Data Engineer

Eve

12h•$185,000 - $210,000

About The Position

Eve is hiring a Staff Data Engineer to own production data pipelines and help build the data platform supporting its rapidly growing legal technology business. The platform has a medallion architecture on a Terraform-managed Snowflake footprint, and the role will develop ingestion and orchestration, reliability practices, access and governance, and standards for contributors. Data includes product usage, case and firm information, and go-to-market systems, and supports analytics, AI agents, and leadership decisions. The Staff Data Engineer will own pipelines end to end, from ambiguous requests through production. This includes improving incremental processing and data freshness, determining how sources are ingested and how schema changes are handled, implementing observability and alerting, and responding to pipeline incidents. The role has meaningful influence over platform design and works on the central data team, reporting to the Head of Data Engineering, who reports to the CEO. The position also supports the foundational modeling layer used by Analytics Engineers, creates safe development environments and CI for analyst contributions, administers data tooling, and documents systems and processes. AI-assisted development is part of the work, with tools and generated workflows subject to the same review standards as other contributions.

Requirements

At least 5 years of data engineering experience owning production systems used by others.
Strong Python and SQL skills.
Production experience with ingestion tools such as Fivetran or similar, orchestration such as the dbt platform, GitHub Actions, or Airflow, and cloud infrastructure.
Solid Snowflake experience, including access control, warehouse sizing, query performance, and cost management.
Practical dbt experience with incremental models, testing, macros, and a git-based workflow with CI.
Experience building SCD tables from multiple sources.
Comfort working in a Terraform-managed environment where infrastructure changes go through code review rather than the Snowflake console.
Experience establishing reliability practices where none existed, including alerting, freshness SLAs, incident response, and 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.
Ability to explain technical issues, impact, and resolution timing to stakeholders without jargon.
Comfort building in an environment without an established playbook.

Nice To Haves

Experience in a regulated or high-sensitivity data environment, such as legal, healthcare, or financial services.
Experience with streaming or near-real-time ingestion and judgment about when it is worthwhile.
Exposure to Iceberg, Parquet, or unstructured data at scale.
B2B SaaS experience, especially selling to small and mid-sized businesses or professional services firms.

Responsibilities

Own ingestion through Fivetran, third-party connectors, and custom extraction where connectors do not exist.
Own orchestration across the dbt platform and GitHub Actions; manage materialization strategy and model performance; migrate critical models from nightly full rebuilds to incremental patterns; and reduce data latency.
Build source-schema change detection that alerts when upstream field changes occur before they affect reports.
Establish observability, including freshness SLAs for critical tables, failure and drift alerting, and an incident path with clear ownership.
Respond to pipeline failures and drive fixes upstream to prevent recurrence.
Manage pipeline compute costs and make freshness-versus-spend tradeoffs explicit.
Share Snowflake administration responsibilities, including role-based access, security and network policies, data masking, PII controls, and storage organization.
Extend the medallion architecture and Terraform-managed footprint, including full separation of development and production environments.
Contribute to foundational modeling patterns, including source-to-staging, conformed dimensions, shared entities, and SCD patterns.
Build and maintain development environments and CI that let analysts safely contribute models, and review their contributions.
Build tooling and setup that help new engineers and analysts become productive quickly.
Administer data tooling, including access, integrations, and connections between systems.
Use AI-assisted development, including Claude Code skills, agents, and evals, while maintaining appropriate review standards.
Document work as the platform is built.
Communicate incidents to stakeholders, explaining what broke, its impact, and expected resolution in clear language.