An ideal fit for this role would be someone who has experience in Data Engineering and transitioned to design and build advanced data science and AI solutions across the enterprise. This role operates at the intersection of data science, data engineering and AI engineering, driving high‑impact use cases in machine learning, and Generative AI while ensuring responsible, secure, and scalable adoption.
This is a hands‑on individual contributor role with broad influence across teams, platforms, and leadership stakeholders.
Key Responsibilities:
AI & Advanced Analytics Enablement
- Drive adoption of Generative AI and LLM‑based solutions, including RAG patterns, semantic search, intelligent automation, and agent‑based workflows
- Lead the design and development of advanced data science solutions, including predictive models, optimization techniques, and AI‑driven insights
- Define best practices for model selection, feature engineering, evaluation, and interpretability across enterprise use cases
Data Platform & Architecture Leadership
- Lead the end‑to‑end data platform architecture, ensuring scalability, performance, cost efficiency, and security
- Define and enforce enterprise data modeling standards (domain‑aligned models, medallion architecture, semantic layers) across analytics, operational, and AI use cases
- Drive optimization strategies and guide adoption of AI capabilities.
Enterprise Integrations & Data Products
- Own data product thinking with consumer‑centric design for downstream teams (analytics, apps, AI)
- Design and oversee reliable, secure data ingestion and integration patterns (APIs, streaming, batch, event‑driven) for internal and external systems
- Ensure integrations align with identity, security, and access governance standards (RBAC, service principals, OAuth‑based access)
Strategic Impact & Thought Leadership
- Shape and communicate the long‑term data and AI platform vision, balancing innovation with stability and governance
- Proactively identify modernization, automation, and AI‑driven opportunities that deliver measurable business value
- Represent data engineering in leadership forums and architecture reviews as a trusted technical authority
Required Qualifications:
- 12+ years of experience in data engineering, data science, or analytics engineering roles within large‑scale enterprise environments
- Proven experience operating at a Principal / Architect level, influencing platform and architecture decisions beyond a single team or project
- Strong understanding of modern data warehousing and lakehouse architectures, including medallion (Bronze/Silver/Gold) or domain‑oriented models and exposure to cloud data platforms like Snowflake
- Experience in statistical modeling, machine learning, and predictive analytics
- Advanced proficiency in Python and common data science libraries
- Experience building Generative AI and LLM‑based solutions
- Experience with RAG architectures, embeddings, vector databases, or agent‑based AI systems
- Strong understanding of prompt engineering, model evaluation, and AI solution trade‑offs
- Hands‑on experience with enterprise data modeling (Dimension, Data Vault modeling)
- Strong understanding of data security, access control, and governance, including RBAC, secrets management, and auditability
- Familiarity with Enterprise Infrastructure, ServiceNow, CMDB is a plus
