Data Engineer

Role Overview

Benchling is building AI & Data Engineering (AIDE), a small, autonomous team within our Security & IT organization. AIDE owns internal AI tooling, adoption, AI-assisted workflows, cross-functional agentic AI applications, and enterprise data engineering, analytics architecture, and source-of-truth datasets.

This is a data engineering role focused on building and operating reliable, production-grade data pipelines and warehouse infrastructure. You will own core pipelines end-to-end (ingestion, transformation, warehouse, and the BI/analytics layer), partner on data architecture, and help build the trusted data foundation that AIDE's AI-adoption and agentic AI work depends on.

Responsibilities

• Own core data pipelines end to end: Build and operate the ELT pipeline that moves data from Benchling's product, Salesforce, and third-party systems into Snowflake, modeled with dbt, adhering to production standards (testing, monitoring, schema versioning).
• Build the data foundation for AIDE's AI initiatives: Partner with AIDE's AI engineering side to make governed, trustworthy data available for agentic AI tooling and internal AI applications.
• Own data governance and pipeline health: Maintain Snowflake access controls (RBAC), monitor data quality, uphold PII-handling and data-access policies, and manage warehouse cost and performance.
• Contribute to platform strategy: Weigh in on structural decisions such as warehouse architecture and semantic layer/metrics store design.

Qualifications

• 3+ years of professional experience building and operating production data pipelines (ingestion, transformation, and modeling) into a cloud data warehouse.
• Strong SQL and Python skills; hands-on experience with data modeling methodologies and tools, preferably dbt.
• Experience applying software engineering practices to data systems (version control, code review, CI/CD, automated testing) and comfort working with cloud infrastructure (AWS or similar).
• Experience with Snowflake or a comparable modern cloud data warehouse in production.
• Comfort with orchestration tooling (Airflow or similar) for scheduled data jobs.
• Track record of supporting multiple stakeholders across departments such as Sales, CS, Product, and Finance.
• Understanding of data privacy, governance, quality, and testing frameworks.
• Strong communication skills; comfortable translating ambiguous requests from non-technical stakeholders into scoped data solutions.
• Comfortable working in a small, fast-moving, still-forming team.
• Interest in learning more about life science (prior knowledge is not required).

Nice to Have

• Familiarity with product behavioral data and a modern BI tool (Sigma, Omni, Looker, Tableau) deployed in a self-service model.
• Experience with product/usage analytics instrumentation and event-taxonomy governance.
• Familiarity with GTM analytics tools such as Salesforce.
• Exposure to AI-usage telemetry, LLM observability data, or supporting AI/ML tooling with curated data.
• Background in enterprise SaaS, life sciences, or biotech.
• Experience building or maintaining a metrics layer.

Work Arrangement

Benchling offers a hybrid work arrangement requiring employees to be on-site 3 days per week (Monday, Tuesday, and Thursday) in San Francisco, CA.