Description
We are rebuilding biotech for the AI era.
When a breakthrough is delayed, the world waits. Getting a molecule from discovery to patients, or a crop from lab to field, involves thousands of slow, manual, disconnected steps. AI has the potential to change this, compressing decades of R&D work into years. But that only happens when clean, structured scientific data and AI are built into how science gets done.
Benchling is the AI platform for biotech R&D. Scientists use Benchling to design experiments, capture structured data, and run AI agents and models directly in their workflows. Over 200,000 scientists around the world trust Benchling to power their most important work, from academic labs to Sanofi, Moderna, and more than half of the world's top 50 biopharma.
Compensation
- $153K – $207K • Offers Equity
U.S. Benefits Full-time U.S. employees enjoy a comprehensive benefits program including equity, health, dental, vision, 401(k)+ employer match, wellness, commuter, and more.
ROLE OVERVIEW
This is a data engineering role , we want someone who builds and operates reliable, production-grade data pipelines and warehouse infrastructure, not a data scientist focused on modeling or analysis.
This role exists because AIDE's data function supports the whole company , GTM, Customer Success, Product, Finance, and beyond , not just one team, and the team needs to grow to support these initiatives as we expand the team’s scope and portfolio. You'll own core pipelines end to end (ingestion, transformation, warehouse, and the BI/analytics layer on top), partner with the rest of the data team on the team's data architecture, and help build the trusted data foundation that AIDE's AI-adoption and agentic AI work increasingly 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, and built to production standards , testing, monitoring, schema versioning , that hold up as usage scales.
- Build the data foundation for AIDE's AI initiatives: Partner with AIDE's AI engineering side to make governed, trustworthy data available for the agentic AI tooling and internal AI applications the team ships.
- Own data governance and pipeline health: Maintain Snowflake access controls (RBAC), monitor data quality, uphold PII-handling and data-access policy, and manage warehouse cost and performance as usage grows.
- Contribute to platform strategy: Weigh in on bigger structural decisions , warehouse architecture, semantic layer/metrics store design, alongside the rest of the data and AI engineering team.
QUALIFICATIONS
- 3+ years of professional experience building and operating production data pipelines , ingestion, transformation, and modeling data into a cloud data warehouse.
- Strong SQL and Python skills; hands on experience with data modeling methodologies and tools, preferable with 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) supporting production pipelines.
- 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 supporting many stakeholders across departments such as Sales, CS, Product, Finance, rather than a single internal customer.
- Understanding of data privacy, governance, quality, and testing frameworks and best practices.
- Strong communication skills; comfortable translating ambiguous requests from non-technical stakeholders into a scoped, buildable data solution.
- Comfortable in a small, fast-moving, still-forming team , AIDE only stood up in its current form in mid-2026 and is actively defining its own processes.
- 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.
HOW WE WORK
We offer a flexible hybrid work arrangement that prioritizes in-office collaboration. Employees are expected to be on-site 3 days per week (Monday, Tuesday, and Thursday).