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Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Build and operate reliable cloud infrastructure, developer platforms, CI/CD systems, observability, and production workloads. Support applications, data systems, ML pipelines, and AI workloads across development, staging, and production. Establish SLOs, monitoring, incident response, automation, infrastructure-as-code practices, and operational standards. Collaborate with Product Engineering, Data Engineering, Data Science, and Security while mentoring engineers and participating in support rotations.

Build and operate backend-focused production software, APIs, integrations, databases, distributed systems, and asynchronous workflows. Develop AI-enabled and agentic systems with retrieval, tool use, evaluation, permissions, observability, and reliability controls. Own products from definition through iteration, collaborate with data engineering, data science, and SRE, make pragmatic technical tradeoffs, and mentor engineers.

Build and operate production software for active clinical trials, including clinical-system integrations, trial data flows, site activation, monitoring, and signal detection. Design AI-native agents and pipelines with auditability, provenance tracking, and human review in regulated GxP environments. Own multi-quarter technical initiatives, collaborate with clinical, regulatory, QA, data, and infrastructure teams, mentor engineers, and improve validated software delivery workflows.

Leads the data engineering team, including hiring, coaching, technical direction, platform architecture, data pipelines, Snowflake warehousing, governance, quality, observability, and incident support. Partners with Data Science, Analytics, Clinical Operations, Biostatistics, and Business Development to deliver trusted data products for clinical operations, asset evaluation, reporting, and machine learning. Oversees budgeting, resourcing, documentation, technical programs, and responsible use of AI engineering tools.

Lead the infrastructure and SRE team, setting technical direction for reliable, secure, and scalable engineering systems. Hire, coach, and develop engineers; oversee multi-cloud infrastructure, Kubernetes, IaC, CI/CD, observability, incident response, support rotations, and regulated workloads. Partner across engineering, data science, security, and operations to improve developer experience and enable safe AI-assisted software delivery. Manage infrastructure roadmaps, budgets, resourcing, documentation, and organizational reliability practices.

Lead the strategy and execution of real-world data capabilities, focusing on data lifecycle management, vendor relationships, and collaboration with various teams to deliver analysis-ready datasets.

The Senior Data Engineer will transform real-world data into analytics-ready datasets, build scalable pipelines, and conduct analyses to support scientific and business decisions, collaborating closely with data science teams.

Lead bioanalytical strategy and execution for drug development programs, manage CROs, oversee assay validation and data review for clinical trials.

Leads the strategy for scaling data science analyses used in pharmaceutical asset acquisition and diligence. Directs cross-functional Data Science and Engineering teams, determines which one-off analyses should become landscape analyses or durable tools, sets roadmaps, evaluates statistical and technical validity, identifies AI opportunities, and connects scientific findings to business impact, asset valuation, and sourcing decisions.