Job Description
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Company Overview
A leading AI and data platform company focused on transforming biotechnology research and development. Its technology enables scientists to design experiments, capture structured scientific data, and incorporate AI tools into research workflows. The platform is used by organizations across academia, biotechnology, and pharmaceutical research to accelerate the development of innovative products.
Responsibilities
- Build and operate core ELT pipelines that move data from product systems, CRM platforms, and third-party sources into Snowflake.
- Develop and maintain data transformation models using dbt and production-grade engineering practices, including testing, monitoring, and schema versioning.
- Build a reliable data foundation that supports internal AI tooling, agentic AI applications, and AI-assisted workflows.
- Maintain data governance and pipeline health, including Snowflake access controls, data quality monitoring, PII handling, and data-access policies.
- Manage data warehouse performance and costs as usage and organizational requirements scale.
- Contribute to data platform strategy, including warehouse architecture and semantic layer or metrics-store design.
- Own data pipelines end to end across ingestion, transformation, warehousing, and the BI/analytics layer.
- Partner with data and AI engineering teams to establish trustworthy, governed datasets that support company-wide initiatives.
- Translate ambiguous requirements from non-technical stakeholders into clearly scoped and buildable data solutions.
Qualifications
- 3+ years of professional experience building and operating production data pipelines, including ingestion, transformation, and data modeling within cloud data warehouses.
- Strong SQL and Python skills.
- Hands-on 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 working with cloud infrastructure such as AWS to support production data pipelines.
- Production experience with Snowflake or a comparable modern cloud data warehouse.
- Familiarity with orchestration tools such as Airflow or similar technologies for scheduled data jobs.
- Experience supporting multiple stakeholders across departments such as Sales, Customer Success, Product, and Finance.
- Understanding of data privacy, governance, data quality, and testing frameworks.
- Strong communication skills and the ability to translate business requirements into technical data solutions.
- Ability to work effectively within a small, fast-moving, and evolving engineering team.
- Interest in learning about life sciences or biotechnology; prior industry knowledge is not required.
Nice to Have
- Familiarity with product behavioral data and modern BI platforms such as Sigma, Omni, Looker, or Tableau.
- Experience with product and usage analytics instrumentation and event-taxonomy governance.
- Familiarity with go-to-market analytics platforms such as Salesforce.
- Exposure to AI-usage telemetry, LLM observability data, or data infrastructure supporting AI/ML tooling.
- Experience in enterprise SaaS, life sciences, or biotechnology.
- Experience building or maintaining a metrics layer.
U.S. Benefits
Full-time U.S. employees may receive a comprehensive benefits program that includes equity, health, dental and vision coverage, 401(k) with employer match, wellness benefits, commuter benefits, and additional offerings.
Compensation is determined using a market-based approach and may vary based on job-related skills, experience, qualifications, interview performance, and work location.
Disclaimer
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