Staff Data Engineer

Role overview

A Staff-level data engineer is needed to set the technical direction for a fast-scaling data platform that backs an AI-native product used by plaintiff law firms. The stack is a medallion warehouse architecture on Snowflake with a Terraform-managed footprint, and the access model, ingestion, orchestration, and reliability layers are still being built out. Analytics engineers and business analysts model on top of the platform, and AI agents are increasingly querying it directly, which raises the stakes on security, freshness, and governance.

Responsibilities

- Design and own a role-based access model with least-privilege grants across medallion layers, including scoped access for service accounts, BI tools, and AI agents rather than broad inherited roles. - Manage grants as code and run the access review cycle so the process itself generates audit evidence. - Administer Snowflake: security and network policies, data masking, PII controls, storage organization, compute cost, and retention. - Extend the Terraform-managed footprint, owning state, module design, environment promotion, and full separation of development and production. - Own ingestion through Fivetran, third-party connectors, and custom extraction, defining what is allowed to land and building source-schema change detection. - Run orchestration across dbt and GitHub Actions, drive incremental model patterns, and stand up freshness SLAs, alerting, and a clear incident path. - Define dbt project architecture, CI and testing standards, and foundational modeling patterns (conformed dimensions, SCD tables, staging patterns).

Requirements

- 8+ years in data engineering, with time at a staff or senior IC level setting technical direction others followed. - Deep Snowflake administration, including building a role-based access model from scratch with masking, PII controls, and cost management. - Strong Python and SQL, with production experience across ingestion (Fivetran or similar), orchestration (dbt, GitHub Actions, or Airflow), and cloud infrastructure. - Advanced dbt: modeling patterns, macros, incremental models, testing, slim or state-based CI, and SCD tables from multiple sources. - Experience with layered warehouse architecture (medallion or equivalent) and Terraform, including state and environment promotion. - Track record building a reliability practice from zero, including freshness SLAs, alerting, incident response, and schema change detection. - Proficiency with AI-assisted development workflows and comfort integrating tools through MCP-style patterns.

Nice to have

- Experience in a regulated or high-sensitivity data environment such as legal, healthcare, or financial services. - Experience supporting ML or GenAI workloads, including feature stores, unstructured data, or Snowflake Cortex. - Background in B2B SaaS, especially selling to small and mid-sized businesses or professional services firms.

Benefits and work setup

- US salary range of $220,000-$300,000. - Competitive salary and equity package. - 401(k) with employer matching. - Health, dental, vision, and life insurance, plus short- and long-term disability. - Commuter benefits for in-office employees and a workplace setup reimbursement. - Telecomm stipend and an autonomous work environment. - Flexible time off plus holidays, with quarterly team gatherings.