Director, Data Engineering & Analytics
- Technical Leadership & Architecture (The "Player")
- Hands-on Engineering: Architect and maintain robust data pipelines using Airflow and Python. Actively contribute to the codebase and perform code reviews to ensure high standards.
- Infrastructure Management: Oversee container orchestration using Kubernetes and manage CI/CD workflows via GitHub Actions to ensure seamless deployment.
- Data Modeling: Design scalable data models that can ingest and harmonize disparate data sources, including complex scientific data sets, and consumer feedback.
- Tech Stack Evolution: Evaluate and implement modern tools (e.g., Ingestion tools, Cloud Data Warehouses, ML Ops, and AI Ops) to future-proof our capabilities.
- Strategy & Team Management (The "Coach")
- Strategic Roadmapping: Act as the Product Owner for the data platform. Partner with R&D, Supply Chain, and Quality leaders to define KPIs and build a roadmap that directly impacts product quality and innovation.
- Team Mentorship: Lead, mentor, and grow a team of analytics engineers and data scientists. Foster a culture of engineering excellence, curiosity, and scientific rigor.
- Data Governance: Establish and enforce data governance policies to ensure the integrity, reproducibility, and security of scientific data (essential for regulatory and quality compliance).
- Translation: Translate complex technical challenges into clear insights for non-technical stakeholders, ensuring data is accessible and understandable across the organization.
- Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related scientific field.
- Experience:
- 7+ years of experience in scientific and/or data-related roles, with a focus on product quality and R&D applications.
- The "Stack": Proven expertise in:
- Languages: Advanced Python and SQL skills.
- Orchestration: Apache Airflow (or similar workflow management tools).
- DevOps/Infra: Terraform, Kubernetes, Docker, and CI/CD (specifically GitHub Actions).
- Version Control: Git flow best practices.
- Master’s degree in Computer Science, Data Science, Engineering, or a related scientific field.
- Experience with Cloud Platforms (GCP).
- Familiarity with Modern Data Warehouses (Snowflake).
- Experience implementing and maintaining Machine Learning models for formulation/optimization.
- Background in Life Sciences, biotech, or consumer goods R&D.
- Domain Knowledge: Experience working with scientific, R&D, clinical, or manufacturing data is highly preferred. You understand that "data integrity" in a science context means reproducibility and precision.
- Soft Skills: Excellent ability to translate complex data into practical insights for a scientific audience. Strong communication and collaboration skills to work effectively with both technical and non-technical stakeholders.
- site
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