About the role
Lead the engineering, delivery, and operational excellence of the enterprise data platform, defining engineering standards and best practices across Snowflake, dbt, PySpark, and Python. Ensure all data solutions are scalable, secure, reusable, GxP-compliant, and built using modern DevOps and DataOps practices.
Responsibilities:
- Define and enforce engineering standards, coding practices, and GxP-compliant software development processes.
- Lead development of data pipelines, ingestion frameworks, reusable data products, and semantic models using Snowflake, dbt, and Python.
- Own DevOps and CI/CD, including Git workflows, automated testing, deployment, and release management.
- Implement DataOps capabilities, including monitoring, observability, incident management, and cost optimization.
- Establish data quality frameworks, data contracts, and reusable engineering patterns.
- Review code, mentor engineers, and provide technical leadership across internal teams and delivery partners.
- Collaborate with architects, product owners, and business stakeholders to deliver high-quality data solutions.
Requirements:
- Exp: 7+ years.
- Strong expertise in Snowflake (or Databricks), dbt, PySpark, Python, SQL, ETL/ELT, and cloud platforms (AWS or Azure).
- Experience with CI/CD, Git, DevOps, DataOps, data quality, and data governance.
- Good understanding of pharmaceutical domains, including manufacturing, supply chain, regulatory, and quality.
- Knowledge of GxP, GAMP, ALCOA+, and regulated software delivery practices.
- Excellent problem-solving, communication, stakeholder management, and mentoring skills.