Senior AI & Analytics Engineer

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 with B2C, product, or customer data and applying metrics such as retention, conversion, funnel performance, cohort analysis, and customer lifetime value to influence business decisions.

      • Production AI development: Demonstrated hands-on experience building and deploying at least one LLM-powered application used by real users, leveraging techniques such as RAG, tool or function calling, or AI agents.

      • Agent frameworks: Experience with at least one AI orchestration or agent framework, such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, or an equivalent technology.

      • Data platforms and analytics tools: Familiarity with modern data platforms, analytical modeling, and business intelligence tools. Experience with BigQuery, Microsoft Fabric, Power BI, Databricks, Snowflake, Google Analytics, or OpenMetadata is an advantage.

      • AI evaluation and governance: Knowledge of AI evaluation, monitoring, guardrails, or responsible AI practices is desirable.

      • Conversational analytics: Experience developing analytics copilots, text-to-SQL applications, natural-language data interfaces, semantic layers, or governed enterprise metrics is a plus.

      • AI and infrastructure technologies: Familiarity with vector databases, semantic search, MCP server development, Azure AI Foundry, Azure OpenAI, Claude, Docker, Kubernetes, CI/CD, or observability tools is beneficial.

      • Communication skills: Ability to explain technical architecture, analytical findings, and complex concepts clearly to stakeholders with varying levels of technical expertise.

      • Problem-solving and collaboration: Strong analytical thinking, ownership, adaptability, and a practical approach to building, testing, and improving solutions.

      • Remote collaboration: Ability to work effectively in a global, remote-first environment using both synchronous and asynchronous communication.

      Benefits:

      • Remote work: Remote opportunity based in India within a globally distributed working environment.

      • High-impact engineering: Build production-grade AI applications and data systems that directly support business intelligence and decision-making.

      • Applied AI innovation: Work with modern LLMs, agent frameworks, retrieval-augmented generation, and natural-language analytics.

      • Technical ownership: Take responsibility for data pipelines, governed metrics, reusable tooling, and end-to-end AI-powered solutions.

      • Cross-functional collaboration: Partner with product, growth, business, and engineering teams to solve meaningful operational and analytical challenges.

      • Professional growth: Expand expertise across analytics engineering, data architecture, AI application development, and production systems.

      • Inclusive culture: Join a collaborative environment that values curiosity, innovation, accountability, continuous learning, and diverse perspectives.

      • Compensation and additional benefits: Salary range, healthcare coverage, paid leave, bonuses, and other specific benefits were not disclosed in the available job description and should be confirmed during the hiring process.