Lead Data Engineer

We are looking for a Lead Data Engineer (Manager) to drive data engineering delivery for a leading pharmaceutical client in Foster City. This role requires a strong balance of technical understanding, consulting mindset, and delivery leadership. The individual will work closely with client stakeholders, data scientists, and engineering teams to translate business requirements into scalable data solutions. While a solid understanding of modern data engineering practices is important, this role focuses primarily on solution oversight, stakeholder engagement, and managing delivery teams, rather than deep hands-on development. The ideal candidate will bring experience working in the pharmaceutical domain, demonstrate strong consulting capabilities, and effectively lead cross-functional teams to deliver high-impact analytics and data platform initiatives.

Roles & Responsibilities

As Lead Data Engineer (Manager), you will::-

• Lead and manage data engineering delivery for client engagements within the pharmaceutical domain
• Act as the primary bridge between client stakeholders and engineering teams, translating business needs into technical solutions.
• Provide architectural guidance and oversight on data platforms, pipelines, and data models.
• Ensure successful delivery of data engineering initiatives supporting analytics, reporting, and data science use cases.
• Drive collaboration across data engineers, data scientists, analytics teams, and client stakeholders.
• Review solution approaches and ensure adherence to data engineering best practices and platform standards.
• Manage and mentor data engineering teams, ensuring quality delivery and professional development.
• Provide consultative recommendations on data strategy, platform improvements, and analytics enablement.
• Ensure alignment between technical solutions and business objectives for the client engagement

Skills Required

• Strong stakeholder management and client-facing communication skills.
• Proven ability to lead delivery teams and manage cross-functional projects.
• Ability to translate complex business requirements into data and analytics solutions.
• Experience working in consulting or client-facing delivery environments
• Strong understanding of data engineering concepts, including data pipelines, data modeling, and modern data platforms.
• Familiarity with cloud data ecosystems (AWS, Azure, or GCP)
• Exposure to modern data technologies such as Snowflake, Databricks, Spark, or similar platforms.
• Working knowledge of Python and SQL to guide technical teams and review solution approaches.
• Experience working in the pharmaceutical or life sciences industry is strongly preferred.
• Exceptional communication, presentation, and stakeholder management skills.
• Familiarity with pharma data domains such as commercial data, patient data, clinical data, or real-world evidence is a plus.