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Benchling is an AI platform for biotech R&D used by over 200,000 scientists globally, including major pharma companies like Sanofi and Moderna. The company is rebuilding biotech for the AI era by combining structured scientific data with AI agents and models directly in research workflows.
You'll join AIDE (AI & Data Engineering), a newly formed autonomous team within Security & IT that owns internal AI tooling, company-wide agentic AI applications, and enterprise data engineering. This role focuses on building and operating production-grade data infrastructure—not data science or modeling.
Key responsibilities include:
- Own core data pipelines end-to-end: Build and operate ELT pipelines moving data from Benchling's product, Salesforce, and third-party systems into Snowflake, modeled with dbt, built to production standards (testing, monitoring, schema versioning) that scale reliably.
- Build the data foundation for AIDE's AI initiatives: Partner with AI engineers to make governed, trustworthy data available for agentic AI tooling and internal 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 around warehouse architecture, semantic layer design, and metrics store implementation alongside the data and AI engineering team.
You'll support multiple stakeholders across GTM, Customer Success, Product, Finance, and beyond—not a single internal customer. AIDE is a small, fast-moving team that only stood up in mid-2026 and is actively defining its own processes.
Required: 3+ years building and operating production data pipelines; strong SQL and Python; hands-on data modeling (preferably dbt); software engineering practices applied to data systems (version control, code review, CI/CD, testing); production experience with Snowflake or comparable cloud data warehouse; orchestration tooling (Airflow or similar); multi-stakeholder support across departments; understanding of data privacy, governance, quality, and testing; strong communication skills translating ambiguous requests into scoped solutions; comfort in a small, forming team; interest in learning life science (prior knowledge not required).
Nice-to-have: Product behavioral data familiarity; modern BI tool experience (Sigma, Omni, Looker, Tableau) in self-service model; product/usage analytics instrumentation; GTM analytics tools.
