Director of Data Engineering
i-Pharm Consulting, San Diego, CA, United States
Director of Data Engineering (Life Sciences)
San Diego, CA | United States
Make sure to apply quickly in order to maximise your chances of being considered for an interview Read the complete job description below.
About the Organization A global life sciences organization focused on developing innovative therapies for complex and underserved diseases. The company operates at the intersection of advanced science, data, and technology, supporting research, development, and commercialization at scale. With a growing pipeline and international footprint, the organization emphasizes scientific rigor, collaboration, and meaningful patient impact. Key Responsibilities: Platform & Architecture Leadership Lead the design, automation, and optimization of cloud data environments across development, test, and production. Own the Databricks-based data engineering platform, leveraging native capabilities to support scalable analytics and AI workloads. Define and enforce engineering standards for ingestion, transformation, orchestration, monitoring, testing, and documentation. Implement modern platform practices including infrastructure as code, CI/CD, observability, cost optimization, and MLOps. Ensure security, compliance, and performance for production-grade data pipelines. Data Domains & Enablement Support analytics and data pipelines across multiple domains, including: Research data , enabling scalable analytics platforms. Clinical data , supporting complex, compliant data flows for clinical studies. Commercial data , enabling reporting and insights for product launches and commercialization. Enterprise data , integrating finance, HR, and operational systems. Partner with data scientists, analysts, and business teams to ensure data solutions are trusted, accessible, and fit for purpose. Enable AI- and analytics-ready datasets for advanced modeling and automation. DevOps, Operations & Reliability Establish best practices for deployment, monitoring, alerting, and incident management. Act as a senior escalation point for complex data engineering and platform challenges. Drive continuous improvement of platform reliability, performance, and cost efficiency. Leadership & Team Development Lead and coordinate external delivery partners to ensure alignment with internal standards and long-term maintainability. Build, mentor, and grow a high-performing data engineering team. Provide technical direction, coaching, and career development for engineers as the team scales. Collaborate with cross-functional leaders to prioritize initiatives and align delivery with business needs. Required Qualifications & Experience 10+ years of experience in data engineering, platform engineering, or related technical roles. Extensive hands-on experience with
Databricks , including strong understanding of its native capabilities. Strong experience with
Azure cloud services ; AWS experience is a plus. Proven leadership in defining architecture, standards, and engineering best practices. Experience working with complex, regulated data environments. Strong communication and stakeholder engagement skills. Bachelor’s degree in Engineering, Computer Science, or a related field (or equivalent experience). Preferred Qualifications & Experience Experience in the
life sciences, biotechnology, or pharmaceutical industry . Exposure to clinical, research, commercial, or real-world evidence data. Experience with enterprise systems such as
SAP, CRM, ERP, finance, or HR platforms . Background in analytics platforms, data products, or real-time/large-scale data systems. Familiarity with Agile/Scrum methodologies. Location & Work Model Strong preference for candidates based in
San Diego, CA
or willing to relocate. Hybrid model with an expectation of
2–3 days per week on-site
for local employees. Remote arrangements may be considered for exceptional candidates, with periodic on-site presence required. Career Growth & Scope Opportunity to lead and scale a growing data engineering function. Significant influence over data platform strategy and technical direction. Exposure to enterprise-wide data initiatives with increasing scale and complexity. xywuqvp Clear path for expanded leadership responsibility as the organization grows.
Make sure to apply quickly in order to maximise your chances of being considered for an interview Read the complete job description below.
About the Organization A global life sciences organization focused on developing innovative therapies for complex and underserved diseases. The company operates at the intersection of advanced science, data, and technology, supporting research, development, and commercialization at scale. With a growing pipeline and international footprint, the organization emphasizes scientific rigor, collaboration, and meaningful patient impact. Key Responsibilities: Platform & Architecture Leadership Lead the design, automation, and optimization of cloud data environments across development, test, and production. Own the Databricks-based data engineering platform, leveraging native capabilities to support scalable analytics and AI workloads. Define and enforce engineering standards for ingestion, transformation, orchestration, monitoring, testing, and documentation. Implement modern platform practices including infrastructure as code, CI/CD, observability, cost optimization, and MLOps. Ensure security, compliance, and performance for production-grade data pipelines. Data Domains & Enablement Support analytics and data pipelines across multiple domains, including: Research data , enabling scalable analytics platforms. Clinical data , supporting complex, compliant data flows for clinical studies. Commercial data , enabling reporting and insights for product launches and commercialization. Enterprise data , integrating finance, HR, and operational systems. Partner with data scientists, analysts, and business teams to ensure data solutions are trusted, accessible, and fit for purpose. Enable AI- and analytics-ready datasets for advanced modeling and automation. DevOps, Operations & Reliability Establish best practices for deployment, monitoring, alerting, and incident management. Act as a senior escalation point for complex data engineering and platform challenges. Drive continuous improvement of platform reliability, performance, and cost efficiency. Leadership & Team Development Lead and coordinate external delivery partners to ensure alignment with internal standards and long-term maintainability. Build, mentor, and grow a high-performing data engineering team. Provide technical direction, coaching, and career development for engineers as the team scales. Collaborate with cross-functional leaders to prioritize initiatives and align delivery with business needs. Required Qualifications & Experience 10+ years of experience in data engineering, platform engineering, or related technical roles. Extensive hands-on experience with
Databricks , including strong understanding of its native capabilities. Strong experience with
Azure cloud services ; AWS experience is a plus. Proven leadership in defining architecture, standards, and engineering best practices. Experience working with complex, regulated data environments. Strong communication and stakeholder engagement skills. Bachelor’s degree in Engineering, Computer Science, or a related field (or equivalent experience). Preferred Qualifications & Experience Experience in the
life sciences, biotechnology, or pharmaceutical industry . Exposure to clinical, research, commercial, or real-world evidence data. Experience with enterprise systems such as
SAP, CRM, ERP, finance, or HR platforms . Background in analytics platforms, data products, or real-time/large-scale data systems. Familiarity with Agile/Scrum methodologies. Location & Work Model Strong preference for candidates based in
San Diego, CA
or willing to relocate. Hybrid model with an expectation of
2–3 days per week on-site
for local employees. Remote arrangements may be considered for exceptional candidates, with periodic on-site presence required. Career Growth & Scope Opportunity to lead and scale a growing data engineering function. Significant influence over data platform strategy and technical direction. Exposure to enterprise-wide data initiatives with increasing scale and complexity. xywuqvp Clear path for expanded leadership responsibility as the organization grows.