Job Description

Data Science Tech Lead

Newark, NJ | Hybrid (3 Days/Week) | Right to Hire | $80–$95/hour (based on experience)

We are seeking a Data Science Tech Lead to join a major financial firm based in Newark, NJ. This is a right-to-hire opportunity for a hands-on technical leader who will drive the architecture and development of enterprise data products using Denodo virtualization and semantic modeling.

Responsibilities

• Lead architecture and development of enterprise data products using Denodo virtualization and semantic modeling.
• Define and govern canonical business models, ontologies, business glossaries, and semantic layers.
• Establish architecture standards, naming conventions, governance frameworks, and development best practices.
• Provide technical leadership to Data Engineers, AI Engineers, LLMOps Engineers, and platform teams.

Denodo & Data Products

• Design and implement Virtual Data Products.
• Develop complex joins, unions, aggregations, and business transformations across multiple source systems.
• Define reusable semantic-layer patterns that support reporting, analytics, APIs, and AI agents.
• Optimize Denodo performance through caching, query pushdown, aggregation strategies, and virtualization best practices.

Integration & APIs

• Design API-first data product architectures.
• Integrate Denodo with AWS services, analytics platforms, data catalogs, AI platforms, and enterprise APIs.
• Support semantic consumption for AI agents and business applications.

Required Qualifications

• 10+ years in Data Engineering, Data Architecture, or Analytics Engineering.
• 5+ years of Denodo implementation experience.
• Experience developing enterprise semantic layers and virtualized data products.
• Strong knowledge of Denodo, AWS (Lambda, Glue, ETL, APIs), data modeling, ontologies, data governance, and API design.
• Experience with Generative AI, agentic AI frameworks, and LLMOps.
• Strong stakeholder engagement and consulting skills.

Preferred Qualifications

• Experience designing AI-ready data platforms.
• Knowledge of AI governance standards.
• Familiarity with Bedrock, OpenAI, Anthropic, Azure OpenAI, or similar platforms.
• Experience implementing enterprise metadata, lineage, and semantic governance frameworks.

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