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Company Overview
As a third-party talent partner, we are representing a leading tech and AI-driven pharma company differentiated by radically more efficient drug development. Founded in 2016, the organization has built advanced technology platforms, processes, and capabilities to accelerate all aspects of drug development and clinical trials. By partnering, acquiring, and in-licensing drugs from pharma companies, research organizations, and biotechs, the company develops programs past clinical proof of concept and beyond to bring new medicines to patients faster and more efficiently. Backed by prominent investors across pharma and tech—including a16z, Sequoia, Sanofi, Thrive Capital, John Doerr, Spark Capital, and SV Angel Growth—the company is dedicated to revolutionizing the pharmaceutical industry through cutting-edge technology.
Responsibilities
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Hire, coach, and develop a team of data engineers, including setting goals, delivering feedback, and managing performance.
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Set the technical direction and roadmap for data engineering, prioritizing work across data quality, governance, scalability, and stakeholder experience in partnership with engineering and business leadership.
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Partner with key stakeholders such as Data Science, Analytics, Clinical Operations, Data Managers, and Business Development on the design and delivery of pipelines and data models that support operational reporting, asset evaluation, and advanced analytics and ML use cases.
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Oversee the architecture and maintenance of the data platform, including ingestion from clinical, operational, and third-party vendor sources, orchestration, transformation, and the Snowflake warehouse.
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Ensure the team establishes and maintains strong data quality, observability, lineage, and documentation practices.
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Own data governance practices for regulated and sensitive data, including access controls, auditability, and traceability appropriate for systems supporting clinical trial operations.
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Review and provide feedback on requirements and design documents for new data products and pipelines.
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Drive knowledge sharing, training, and mentoring on data engineering fundamentals and modern data tooling throughout the organization.
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Own the team’s support rotation coverage for data platform incidents, including staffing, escalation paths, and incident retrospectives.
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Manage team budgeting, resourcing, and reporting for data engineering initiatives.
About You
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7+ years of total experience across hands-on data engineering and people management, including 3+ years directly managing Data Engineers, Analytics Engineers, or similar.
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A track record of hiring, developing, and retaining strong engineers, with the ability to give direct, actionable feedback and set clear expectations.
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Skilled at directing a team that uses AI tools, including LLMs and agentic coding systems, as a core, daily part of how they build.
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Strong point of view on modern data stack tooling: warehousing (Snowflake), orchestration (e.g. Dagster or Airflow), transformation (e.g. dbt), and batch vs. streaming approaches.
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Exceptional collaboration and communication skills to effectively engage stakeholders from diverse disciplines and varying levels of technical expertise.
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Experience managing or coordinating technical projects and programs, including resource budgeting, estimates, tracking, and reporting.
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Working knowledge of data governance, access control, and auditability practices, ideally including regulated or sensitive data (clinical, PHI, or similar).
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Working knowledge of Python, SQL, Docker, GitHub, and Terraform/OpenTofu sufficient to guide and evaluate technical decisions.
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Experience overseeing data quality frameworks, testing, and observability tooling for production data pipelines.
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Experience with pharmaceutical, biotechnology, or broader life-sciences industry data is a plus.
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