CS&D Senior Principal Engineer - Systems - CTI MD Tech (LRL)

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

LRL takes potential marketable products from Research through product registration and approval, enabling MDU's business needs of the future through information and technology solutions. Within this portfolio, Clinical Supply & Delivery (CS&D) owns the systems and platforms that move investigational drug product — from packaging and labeling through distribution and fulfillment — to clinical trial sites, reliably, compliantly, and on time for trial execution. The CS&D Senior Principal Engineer - Systems is a senior individual contributor who owns hands-on technical delivery for these platforms — designing, building, and directly implementing integration patterns, data pipelines, and platform components that translate portfolio direction and roadmap decisions into production systems.

Path/Level: R4 (CS&D Senior Principal Engineer - Systems)

This role is structured as roughly 75% hands-on technical execution — architecting and directly implementing platform components, data pipelines, and integrations — and 25% technical leadership, mentorship, and design guidance for R2/R3 engineers. This is a senior individual-contributor position: candidates should bring sustained, current hands-on engineering depth alongside strong architectural judgment, and should expect to remain an active technical contributor throughout the role.

Key Responsibilities

Hands-On Engineering & Implementation (75%)

• Platform Build & Implementation — design, build, and directly implement production components for CS&D's clinical supply chain systems — integration layers, data pipelines, APIs/microservices, and cloud infrastructure — including critical or complex components, in close collaboration with R2/R3 engineers.

• Data Platform Engineering — build and maintain cloud-native data warehousing, ETL/ELT, and data lake/lakehouse solutions across AWS and Databricks/Delta Lake; make and implement database architecture decisions — relational vs. NoSQL vs. NewSQL selection, schema design, partitioning/sharding, indexing, and performance tuning.

• Integration & API Engineering — build API and microservices integration patterns for data access and cross-system integration; implement monitoring, alerting, and observability for data pipelines; diagnose and resolve pipeline failures and performance bottlenecks.

• AI/Agentic System Build — design and build agentic workflows, LLM integration and orchestration, RAG and vector-store implementations, and AI governance/guardrails; develop and validate proofs-of-concept ahead of production adoption.

• Governance, Risk & GxP/GCP Compliance Implementation — implement audit-readiness, security, and change-control practices in code and infrastructure; produce architecture documentation supporting audit, inspection, and CSV readiness.

• Business & Product Partnership — partner with architects, product managers, and business stakeholders to translate business goals into technical solutions; validate technical feasibility through hands-on spikes and prototypes.

Technical Leadership & Architecture Guidance (25%)

• Architecture Direction — provide design direction and integration patterns for R2/R3 engineering teams, informed by direct, current experience building comparable systems.

• Code & Design Reviews / Mentorship — conduct code and architecture reviews; mentor engineers through pairing, technical guidance, and shared problem-solving; champion engineering excellence and consistent design standards.

• Decision-Making (Implementation-Scoped) — hold design and integration decision authority for the systems owned; approve implementation-level architecture and integration patterns used by R2/R3 teams; escalate — rather than unilaterally resolve — platform-architecture, investment-level, or cross-portfolio risks to CS&D architecture leadership.

• India-Based Technical Leadership — represent CS&D engineering in relevant technical forums; build shared ownership and accountability between the India team and global stakeholders; strengthen India's role as a technical design and engineering center for the platforms owned.

• Talent Development — contribute to hiring and technical talent planning for the engineering teams, including evaluation of candidates' technical depth.

Technical & Strategic Expertise Areas

Cloud-Native Data Platform Architecture (AWS: S3, Redshift, RDS/Aurora, DynamoDB, Glue, DMS, Lambda, EMR, Athena, API Gateway, Step Functions, EventBridge, CloudFormation, CDK, IAM, VPC, CloudWatch) · Data Lake / Data Warehouse / Lakehouse Design · Databricks / Delta Lake · Database Architecture & Design (relational and NoSQL, indexing, partitioning/sharding strategy) · Agentic/AI System Design & Build (LLM orchestration frameworks, RAG, vector databases, LLMOps and AI observability) · Enterprise Architecture Frameworks (TOGAF, ArchiMate, C4 model) · CI/CD & DevOps (GitHub Actions, GitLab CI, Jenkins) · Infrastructure as Code (CloudFormation, Terraform, CDK) · API & Microservices Design · SQL & Dimensional Data Modelling (Redshift, Oracle, SQL Server) · Monitoring, Alerting & Observability · Streaming Platforms (Kafka, Confluent, AWS MSK) · Security & Compliance Architecture (GxP, GCP, CSV) · Cost Optimization (FinOps) & Disaster Recovery/Business Continuity Design · Front-End & Back-End Development

Basic Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).

• 12+ years of hands-on experience in data engineering, data architecture, or enterprise data platform roles, with a sustained, current track record of designing and implementing production systems.

• Strong AWS expertise with hands-on experience across: Data Storage & Warehousing (S3, Redshift, RDS/Aurora, DynamoDB); Data Processing & Integration (Glue, DMS, Lambda, EMR, Athena); API & Orchestration (API Gateway, Step Functions, EventBridge); Infrastructure (CloudFormation, CDK, IAM, VPC, CloudWatch).

• Strong SQL and data modelling expertise (dimensional modelling, star/snowflake schemas) across Redshift, Oracle, and SQL Server platforms.

• Strong experience with CI/CD pipelines for data platforms (GitHub Actions, GitLab CI, Jenkins) and Infrastructure as Code (CloudFormation, Terraform, or CDK).

• Current, hands-on coding proficiency across front-end, back-end, and AWS cloud development, with the ability to build, debug, and deliver production systems directly.

• Ability to speak in specific, concrete detail about systems designed, technical trade-offs made, and outcomes delivered.

• Demonstrated ability to mentor engineers and lead technical discussions through hands-on pairing and code review, without requiring a formal people-manager structure.

Additional Qualifications / Competencies

• Experience in regulated industries (healthcare/pharma, finance, government) with compliance requirements.

• Experience with clinical data, life sciences, or statistical computing domains.

• Hands-on experience designing and building AI/agentic systems personally — including LLM orchestration frameworks (e.g., LangChain, AutoGen, or similar), vector database and RAG architecture, and MLOps/LLMOps pipelines using AWS services such as SageMaker.

• Familiarity with data governance and metadata management frameworks.

• Comfort operating in ambiguity, translating architecture-level decisions into working systems under evolving requirements.

• A portfolio of shipped technical work, relevant open-source contributions, or verifiable examples of production systems delivered end-to-end is a plus.

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