Data Engineer

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Company Overview

A leading technology company is developing an AI platform designed to accelerate biotechnology research and development. Its platform enables scientists to design experiments, capture structured scientific data, and integrate AI agents and models directly into their workflows.

The organization supports more than 200,000 scientists worldwide, including researchers at leading biopharmaceutical companies and academic institutions. Its technology helps modernize scientific workflows and enables organizations to accelerate the development of medicines, agricultural products, materials, and other biotechnology innovations.

The AI & Data Engineering team is a small, autonomous organization responsible for internal AI tooling and adoption, company-wide agentic AI applications, enterprise data engineering, analytics architecture, and trusted source-of-truth datasets.

Key Responsibilities

• Own core data pipelines end to end, including ingestion, transformation, warehouse modeling, and analytics infrastructure.
• Build and operate ELT pipelines that move data from product systems, Salesforce, and third-party systems into Snowflake.
• Develop data models using dbt and maintain production-grade standards for testing, monitoring, and schema versioning.
• Build scalable data infrastructure that can support growing organizational usage and evolving business requirements.
• Partner with AI engineering teams to provide governed and trustworthy data for agentic AI tooling and internal AI applications.
• Maintain data governance practices and ensure appropriate access controls across enterprise data systems.
• Manage Snowflake role-based access control (RBAC), data quality monitoring, and data-access policies.
• Ensure appropriate handling of personally identifiable information (PII) and compliance with data-access requirements.
• Monitor and optimize warehouse performance and costs as data usage scales.
• Contribute to architectural decisions involving warehouse infrastructure, semantic layers, and metrics-store design.
• Collaborate with data and AI engineering teams to establish scalable data architecture and engineering practices.
• Translate business requirements from multiple departments into reliable and maintainable data solutions.
• Apply software engineering practices such as version control, code review, CI/CD, and automated testing to data systems.
• Support scheduled and orchestrated data workflows using Airflow or similar technologies.

Required Qualifications

• 3+ years of professional experience building and operating production data pipelines, including data ingestion, transformation, and 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 reviews, CI/CD, and automated testing.
• Experience working with cloud infrastructure, preferably AWS or similar platforms, supporting production data pipelines.
• Production experience with Snowflake or a comparable modern cloud data warehouse.
• Experience with orchestration tools such as Airflow or similar platforms.
• Experience supporting multiple stakeholders across departments such as Sales, Customer Success, Product, and Finance.
• Strong understanding of data privacy, governance, quality, and testing frameworks and best practices.
• Strong communication skills with the ability to translate ambiguous requirements from non-technical stakeholders into clearly scoped and buildable data solutions.
• Ability to work effectively within a small, fast-moving, and evolving engineering organization.
• Interest in learning more about life sciences; prior industry knowledge is not required.

U.S. Benefits

Full-time U.S. employees may receive a comprehensive benefits program that includes equity, health, dental, vision, 401(k) with employer match, wellness programs, commuter benefits, and additional benefits.

Compensation is determined using a market-based approach. Starting compensation may vary based on job-related skills, experience, qualifications, interview performance, and work location.

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