Lead-data Automation and Excellence

Johnson & Johnson Johnson & Johnson · Pharma · Titusville, NJ +1

Lead Data Automation and Excellence role responsible for leading a technology squad in the plan, design, monitor, build, and operation of high-quality, scalable, and AI-ready data assets. This role provides hands-on technical leadership, drives automation of curating using AI agents for data management, sets engineering standards, and ensures alignment with enterprise architecture. It combines deep data engineering expertise with architectural judgment, mentoring capability, and modern AI for Data management experience.

What you'd actually do

  1. Responsible to drive solution architecture for data across ingestion, curation, and consumption layers.
  2. Improve operational efficiency through optimization of CI/CD pipelines aligned with the enterprise SDLC toolchain.
  3. Apply data engineering best practices alongside strong AI and GenAI literacy, including: Prompt engineering and AI-assisted development, Large Language Models (LLMs) and GenAI concepts, Data profiling and automated asset creation.

Skills

Required

  • 7+ years experience in data engineering
  • 3–5+ years in a senior or technical lead role
  • Strong experience designing, building, and operating data pipelines across batch, streaming, and near-real-time use cases
  • 5+ years experience working with modern layered or medallion architectures
  • Proven ability to mentor engineers, lead by technical influence, and collaborate effectively across product, architecture, and platform teams
  • 3+ years hands-on experience with Kafka, event-driven architectures, and CDC-based ingestion and curation patterns
  • 4+ years experience implementing and optimizing CI/CD pipelines aligned with enterprise SDLC standards for data platforms
  • Working knowledge of AI and GenAI concepts relevant to data platforms, including LLMs, prompt engineering, ontologies, metadata enrichment, and data profiling
  • Strong understanding of data quality, lineage, governance, contextualization, and cataloging to support AI-ready data assets

Nice to have

  • Prompt engineering and AI-assisted development
  • Large Language Models (LLMs) and GenAI concepts
  • Data profiling and automated asset creation

What the JD emphasized

  • AI-ready data assets
  • automation of curating using AI agents
  • modern AI for Data management experience
  • AI Enablement & Data Readiness
  • GenAI literacy
  • AI and GenAI concepts relevant to data platforms, including LLMs, prompt engineering, ontologies, metadata enrichment, and data profiling.
  • data quality, lineage, governance, contextualization, and cataloging to support AI-ready data assets.

Other signals

  • AI-ready data assets
  • automation of curating using AI agents
  • modern AI for Data management experience
  • AI Enablement & Data Readiness
  • GenAI literacy