Overview

Lead AI Data Engineer

Responsibilities

• eCTS is a core clinical supply product within CTI-MD, helping make sure the right investigational medicine reaches the right patient at the right time. Over the next few years, clinical systems will evolve from a database-centric application into an intelligent, cloud-native clinical supply data platform: one where critical business logic is explicit and tested, data is served as governed products, and AI agents work safely alongside people to plan, monitor, and resolve supply events.
• The person in this role will help build the following future state. It is the lens for every responsibility below.
• Logic made explicit
• Business rules held in legacy stored procedures are documented, covered by automated tests, and progressively re-platformed, with AI accelerating analysis and conversion under validated controls.
• Data as a Service
• eCTS data is published as governed, well-documented data products and APIs with clear contracts and SLAs, serving the application, analytics, adjacent clinical systems, and AI consumers.
• AI-ready and agent-enabled
• Data is structured, described, and exposed so AI agents can reason over it and act on it safely, with least-privilege access, human-in-the-loop approvals, and full auditability.
• Cloud-native and as-code
• Database changes, infrastructure, and quality checks flow through automated pipelines, with observability that moves operations from reactive to proactive.
• What We're Really Looking For (Core Needs)
• Two things matter most in this role:
• Own, maintain, and continuously improve eCTS's existing stored procedures and database objects, which carry critical business logic today, while leading their conversion into full-stack and AI-enabled flows using AI-assisted approaches.
• Make embedded business logic explicit: document rules, build automated unit and regression tests for stored procedures and data logic, and create the safety net that makes confident, rapid change possible.
• Own the design, development, and evolution of relational and non-relational data models supporting eCTS and IWRS.
• Write, review, and optimize complex SQL, stored procedures, functions, and scripts, with the depth and care to keep long-standing logic correct and performant.
• Translate product and engineering requirements into robust, scalable database designs.
• Establish and enforce data modeling standards, naming conventions, and design patterns across the team.
• Lead the technical execution of the eCTS database modernization strategy, moving from legacy databases to a modern, cloud-native data platform.
• Design and deliver migration approaches (schema conversion, data mapping, ETL/ELT pipelines, and cutover plans) that minimize disruption to global trial operations.
• Apply AI-assisted tooling to schema conversion, code translation, and data reconciliation, with evaluation and human review built into the process.
• Design for decoupling, using event-driven and change-data-capture (CDC) patterns so IWRS and downstream consumers can evolve independently.
• Partner with architects to evaluate target database technologies, trade-offs, and integration patterns.
• De-risk migration through prototypes, dry runs, reconciliation, and rollback planning.
• Design eCTS data as reusable data products with clear ownership, documentation, data contracts, and SLAs, in line with the CTI-MD Data as a Service (DaaS) direction.
• Build the metadata, lineage, and semantic context that let people and AI systems find, understand, and trust eCTS data.
• Expose governed data access for applications and AI agents through APIs and agent tooling (for example, Model Context Protocol servers), with least-privilege and auditable access patterns.
• Apply retrieval and vector search patterns (for example, embeddings in PostgreSQL) where they add real value to search, support, and decision-making.
• Lead and drive the exploration and delivery of agentic AI solutions across eCTS, working from a strong and growing opportunity pipeline.
• Set technical direction, prototype, and execute agentic solutions hands-on, independently and without hand-holding.

Qualifications

• 6+ years of hands-on database engineering / development experience.
• Experience leading database design and development on non-trivial software products.
• Experience delivering data migration or database modernization efforts.
• Experience working with engineering teams in Agile environments.
• Strong SQL and relational database expertise (e.g., SQL Server, Oracle, PostgreSQL, or equivalent), including performance tuning and query optimization.
• Deep, hands-on comfort with stored procedures and complex database logic, and a genuine willingness to own and improve long-standing legacy objects.
• Solid data modeling skills across transactional and analytical use cases.
• Experience with ETL/ELT and data migration tooling and patterns.
• Working knowledge of cloud data platforms (Azure and/or AWS) and modern managed database services.
• Understanding of database security, high availability, backup/recovery, and monitoring.
• Hands-on experience building AI- or LLM-enabled solutions (for example, agents, retrieval, or AI-assisted code and data workflows), including at least one used by others beyond a personal prototype.
• Experience with database-as-code practices: source control, CI/CD, automated migrations, and database change management.
• Ability to read and reason about application code well enough to partner effectively with engineers on data-related issues.
• Demonstrated ability to work autonomously, driving technical initiatives end to end and influencing other teams without relying on close direction.
• Comfort making sound decisions with incomplete information in a fast-moving technology space, and learning quickly as tools and practices evolve.
• Experience in Pharmaceutical, Life Sciences, Clinical Development, Supply Chain, or Regulatory / Quality domains.
• Understanding of GxP, validation, data integrity, and regulated environments, including risk-based approaches such as Computer Software Assurance.
• Experience with clinical, manufacturing, or laboratory systems (e.g., IWRS / clinical supply systems).
• Experience with agent frameworks, Model Context Protocol, LLM evaluation, or AI observability tooling.
• Experience with automated database testing and migration tools (e.g., tSQLt, pgTAP, Flyway, Liquibase) and infrastructure as code.
• Familiarity with event streaming and CDC, NoSQL, vector databases, lakehouse platforms (e.g., Databricks), or analytics platforms.
• Relevant database, cloud, or AI certifications are a plus.

Education

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical discipline.