Sr Manager, IT Data Engineering

Gilead Sciences•Raleigh, NC

4d•$146,200 - $189,200

About The Position

The Delivery Lead for Kite represents a unique opportunity within the IT Data & AI Center of Excellence (CoE) to drive the adoption of Data, Analytics, and AI capabilities that accelerate Kite's mission of delivering life-changing therapies to patients. You will serve as the primary technical leader aligned to the G&A business function, including Finance, Procurement, IT Services, and other enterprise operations, driving business outcomes through hands-on execution and multidisciplinary expertise. In this role, you will build and deliver production-grade solutions, work directly with stakeholders to translate business needs into clear technical approaches, and operate with speed and agility in complex, fast-paced environments. This role requiring strong technical depth, credibility with senior stakeholders, industry expertise, and an entrepreneurial mindset to navigate ambiguity, solve complex challenges, and deliver sustained business impact over time.

Requirements

Bachelor’s or master’s degree in computer science, Data Engineering, Data Science, or a related field, or equivalent practical experience.
3–6+ years of experience in Data Engineering, with a proven track record of designing and deploying data pipeline solutions at scale.
At least 2+ years of experience building complex data science, AI/ML, or large-scale analytics solutions.
Proficiency in Python and SQL; familiarity with TypeScript/JavaScript or a systems programming language such as Go or Rust.
Experience with Test-Driven Development (TDD), CI/CD pipelines, and modern software engineering best practices.
Experience with CI/CD platforms (e.g., GitHub Actions), containerization technologies (Docker), infrastructure-as-code principles, and cloud-native architecture patterns.
Hands-on experience building and operating data and AI platforms on AWS, including services such as S3, Glue, Lambda, IAM, Bedrock, and Databricks Unity Catalog.
Proficiency with Databricks (Spark), data orchestration platforms (Airflow), Tableau, Genie Spaces, and related business intelligence and analytics technologies.
Hands-on experience with AI coding tools (Claude Code, GitHub Copilot, Cursor, or equivalent) and Cortex AI or comparable LLM-serving platforms.
Strong understanding of how large language models generate and reason about code, with practical experience in prompt engineering as an engineering discipline.
Strong understanding of data privacy, security, and regulatory requirements in healthcare environments.
Experience with secrets management, audit controls, and compliance frameworks, including HIPAA, SOC 2, and 21 CFR Part 11.
Ability to design and operate systems that scale across both traditional ML infrastructure and emerging agentic AI architectures.

Nice To Haves

Experience supporting analytics and AI initiatives within Cell and Gene Therapy (CGT), oncology, rare disease, or specialty pharmaceutical organizations.
Experience designing multi-agent workflows, orchestration patterns, and autonomous systems for enterprise applications.
Familiarity with MCP (Model Context Protocol), agent interoperability frameworks, and AI governance best practices.
Experience supporting high-throughput inference workloads, large-scale batch scoring, low-latency APIs, and horizontally scalable AI/agent platforms.
Experience integrating enterprise platforms and related APIs to enable end-to-end business process automation and analytics workflows.
Experience designing secure, scalable, and production-grade cloud architectures is preferred.

Responsibilities

Design and implement scalable, secure, and high-performance data solutions across a cloud environment, including AWS and Databricks.
Define and enforce data engineering standards, best practices, and architectural patterns.
Collaborate with a highly motivated team to evaluate and integrate emerging technologies that enhance the capabilities of the data platform.
Design, develop, and maintain ELT pipelines using AWS services, Apache Airflow, and Databricks.
Drive platform evolution by identifying opportunities for AI-powered automation and implementing solutions that improve data workflows and operational efficiency.
Manage the development and maintenance of data models and schema designs to support efficient data storage, processing, and retrieval.
Lead schema evolution and data integration processes across source systems and target platforms.
Serve as a subject matter expert for internal stakeholders and business partners by addressing technical inquiries and providing strategic guidance.
Implement and maintain robust data governance, security, and compliance frameworks.
Ensure data quality, security, and integrity through effective validation, cleansing, testing, monitoring, and issue-resolution processes.
Establish data quality metrics, service-level agreements (SLAs), and monitoring mechanisms to ensure the reliability and trustworthiness of enterprise data assets.
Ensure compliance with organizational policies and regulatory requirements related to data privacy, security, and retention.