Note: The job is a remote job and is open to candidates in USA. Benchling is an AI platform for biotech research and development. The Data Engineer will build and operate production-grade data pipelines, warehouse infrastructure, and analytics foundations across the company, while supporting AI initiatives and data governance. The role also partners on data architecture and delivers trusted data solutions for stakeholders across Sales, Customer Success, Product, Finance, and other departments.
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. This is infrastructure the rest of the company builds on, not a one-off project
- 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
Skills
- 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)
- 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
Benefits
- Offers Equity
- Remote work
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