Lead - AI Data Intelligence (ETL,Azure, SQL, Python, Spark, Data Lakehouse) 7 - 11 years exp from SaaS companies

About this job

Zenoti is a leading provider of an all-in-one, cloud-based software solution built for the beauty and wellness industry, powering thousands of salons, spas, and fitness studios globally with comprehensive mobile solutions for bookings, POS, CRM, and inventory management. In the role of Lead - AI Data Intelligence, you will act as a technical leader and strategic advisor to build production-grade Text-to-SQL and Natural Language Query platforms that connect operational systems to cloud lakehouses. A normal day in this role involves partnering directly with business stakeholders to scope analytical workflows, designing governed semantic models and metric ontologies, guiding cross-functional engineering pods, and deploying conversational data products using Python, FastAPI, Databricks, and Amazon Redshift platforms to handle large-scale enterprise data with high reliability and availability.

Responsibilities

• Lead cross-functional engineering pods to design, build, and deploy production Text-to-SQL and Natural Language Query platforms.
• Partner directly with client stakeholders and business leaders to diagnose operational bottlenecks and deliver working solutions.
• Architect governed semantic layers, business ontologies, metric views, and parameter mappings grounded in verified business logic.
• Build robust microservices, REST APIs, and event-driven data pipelines connecting backend systems with modern Python, FastAPI, and cloud lakehouses.
• Collaborate with product managers and engineers to build responsive conversational data experiences with smart disambiguation and interactive filters.
• Implement enterprise AI guardrails, evaluation suites, and observability frameworks to guarantee safety, data privacy, and reliability.
• Champion modern AI-assisted engineering practices and create reusable engineering assets to accelerate team velocity.

Requirements

• 8+ years of experience designing, building, and operating enterprise-scale distributed software and cloud systems with mastery of OOP and .NET/C# REST APIs.
• Deep expertise in SQL Server relational schema design, complex joins, views, and aggregations.
• 2+ years of dedicated, hands-on production experience navigating the AI development lifecycle, including LLM orchestration and agentic architectures.
• Advanced proficiency in Python and FastAPI for developing asynchronous microservices and data validation schemas.
• Practical experience building Text-to-SQL and NLQ systems with strong capability in semantic layer architecture and business ontology modelling.
• Strong technical leadership experience mentoring senior engineers and leading delivery pods for executive stakeholders.
• Bachelor's or master's degree in computer science, Software Engineering, Information Systems, or equivalent practical industry experience.

Good to have

• Hands-on experience building scalable data pipelines on Amazon Redshift and Databricks.
• Exposure to benchmark conversational analytics products like Databricks Genie.
• Familiarity with modern frontend frameworks such as React, Flutter, or Next.js.
• Hands-on familiarity with modern AI-assisted development tools like Cursor, Claude Code, and Copilot.

Who should apply

This role suits experienced software and data engineering professionals with extensive background in distributed systems, AI development lifecycles, and technical leadership. Candidates should have a strong track record of guiding cross-functional teams, partnering with business stakeholders, and building production-grade Text-to-SQL or Natural Language Query platforms. Freshers should not apply, as the position specifically requires 7 to 11 years of experience from SaaS companies and deep prior expertise in software engineering and enterprise AI systems.

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