Senior AI & Analytics Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior AI & Analytics Engineer based in India. This is an exciting opportunity for a senior technical professional to build the data infrastructure and AI-powered applications that transform how businesses access and use insights. In this hands-on role, you will combine analytics engineering, data architecture, and applied AI to create reliable data products and intelligent analytical tools. You will own production-grade data pipelines, reusable datasets, and governed business metrics while developing LLM-powered copilots, agents, and natural-language data experiences. Working closely with product, growth, business, and engineering teams, you will translate complex business questions into scalable, actionable solutions. You will also help automate recurring analyses, improve decision-making, and make trusted insights accessible to both technical and non-technical users. This opportunity is ideal for an engineer who enjoys building practical AI systems, solving real business problems, and delivering measurable impact in a collaborative, innovation-focused environment.

Accountabilities

• Analytics Engineering and Data Foundations

• Build, maintain, and optimize scalable data pipelines, transformations, analytical datasets, and reusable data products using SQL and Python.
• Analyze B2C, product, and customer data to identify trends, performance drivers, behavioral patterns, and actionable business insights.
• Translate business questions into reliable metrics, analytical models, and decision-support solutions.
• Develop a governed metrics layer with consistent definitions for retention, conversion, customer lifetime value (LTV), and other core business indicators.

• Ensure data quality, consistency, documentation, and accessibility across analytical systems and AI-powered applications.

• AI-Powered Analytics and Application Development

• Design, build, and deploy LLM-powered applications, agents, copilots, and automated analytics workflows using retrieval-augmented generation (RAG), tool calling, MCP, and modern agent frameworks.
• Develop natural-language data interfaces that enable product managers, analysts, and business users to ask questions and receive reliable, contextually relevant answers supported by data sources.
• Create automated workflows for recurring analyses, including performance reviews, anomaly investigation, and funnel diagnostics.
• Build reusable AI tools, knowledge sources, documentation, scripts, and MCP servers that enable efficient and safe data extraction, analysis, and visualization.

• Evaluate and continuously improve AI solutions in production, focusing on accuracy, reliability, cost efficiency, monitoring, and safety.

• Systems Integration and Production Engineering

• Integrate data platforms, analytics systems, APIs, AI components, and user-facing workflows into secure, maintainable, production-ready services.
• Contribute to the architecture and design of scalable data and AI systems in partnership with platform and engineering teams.
• Apply engineering best practices to deployment, observability, testing, and ongoing system maintenance.

• Identify opportunities to improve the scalability, performance, reliability, and usability of data and AI capabilities.

• Business Collaboration and Continuous Improvement

• Work directly with product, growth, and business stakeholders to understand analytical needs and deliver solutions that support informed decisions.
• Communicate technical concepts, system designs, and analytical findings clearly to both technical and non-technical audiences.
• Continuously explore emerging AI techniques and tools to improve analytical workflows and accelerate the development of new capabilities.

Requirements

• Professional experience: At least 6 years of hands-on experience in analytics engineering, data engineering, business or product analytics, applied AI, or a related technical discipline.
• SQL and Python: Strong proficiency in SQL and Python, with proven experience building and maintaining production-grade data pipelines and analytical datasets beyond exploratory notebooks.
• System architecture: Experience designing scalable, end-to-end solutions that integrate data platforms, APIs, services, AI components, and user workflows.
• Business and product analytics: Practical experience working …