Data Engineer
Build and operate reliable, production-grade data pipelines, warehouse infrastructure, and trusted datasets supporting company-wide analytics and AI initiatives. The role requires 3+ years of production data engineering experience, strong SQL and Python skills, and experience with Snowflake, dbt, cloud infrastructure, and orchestration.
About the job
Responsibilities
- Build and operate production-grade ELT pipelines ingesting data from Benchling’s product, Salesforce, and third-party systems into Snowflake.
- Model data with dbt and maintain testing, monitoring, schema versioning, and other reliability standards.
- Partner with AI engineering teams to provide governed, trustworthy data for agentic AI tools and internal AI applications.
- Maintain Snowflake access controls, including RBAC; monitor data quality; enforce PII-handling and data-access policies; and manage warehouse cost and performance.
- Contribute to warehouse architecture and semantic layer or metrics-store decisions.
Requirements
- 3+ years of professional experience building and operating production data pipelines, including ingestion, transformation, and warehouse modeling.
- Strong SQL and Python skills.
- Experience with data modeling methodologies and tools, preferably dbt.
- Experience applying software engineering practices to data systems, including version control, code review, CI/CD, and automated testing.
- Experience with cloud infrastructure, such as AWS, supporting production pipelines.
- Production experience with Snowflake or a comparable modern cloud data warehouse.
- Familiarity with orchestration tools such as Airflow.
- Experience supporting stakeholders across multiple departments.
- Understanding of data privacy, governance, data quality, and testing best practices.
- Strong communication skills and ability to translate ambiguous stakeholder requests into scoped data solutions.
- Comfortable working in a small, fast-moving team.
Nice to Have
- Familiarity with product behavioral data and modern BI tools such as Sigma, Omni, Looker, or Tableau.
- Experience with product and usage analytics instrumentation and event-taxonomy governance.
- Familiarity with Salesforce and GTM analytics.
- Exposure to AI-usage telemetry, LLM observability data, or curated data supporting AI/ML tooling.
- Background in enterprise SaaS, life sciences, or biotech.
- Experience building or maintaining a metrics layer.
Compensation and Benefits
- Flexible hybrid work arrangement with in-office collaboration expected 3 days per week: Monday, Tuesday, and Thursday.
Skills
SQL, Python, dbt, Snowflake, AWS, Airflow, CI/CD, Data Modeling, Data Governance, Data Quality, Salesforce, Tableau
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