Senior AI & Analytics Engineer

Accountabilities: •

Analytics Engineering and Data Foundations

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Build, maintain, and optimize scalable data pipelines, transformations, analytical datasets, and reusable data products using SQL and Python.

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Analyze B2C, product, and customer data to identify trends, performance drivers, behavioral patterns, and actionable business insights.

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Translate business questions into reliable metrics, analytical models, and decision-support solutions.

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Develop a governed metrics layer with consistent definitions for retention, conversion, customer lifetime value (LTV), and other core business indicators.

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

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AI-Powered Analytics and Application Development

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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.

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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.

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Create automated workflows for recurring analyses, including performance reviews, anomaly investigation, and funnel diagnostics.

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Build reusable AI tools, knowledge sources, documentation, scripts, and MCP servers that enable efficient and safe data extraction, analysis, and visualization.

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

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Systems Integration and Production Engineering

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Integrate data platforms, analytics systems, APIs, AI components, and user-facing workflows into secure, maintainable, production-ready services.

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Contribute to the architecture and design of scalable data and AI systems in partnership with platform and engineering teams.

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Apply engineering best practices to deployment, observability, testing, and ongoing system maintenance.

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

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Business Collaboration and Continuous Improvement

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Work directly with product, growth, and business stakeholders to understand analytical needs and deliver solutions that support informed decisions.

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Communicate technical concepts, system designs, and analytical findings clearly to both technical and non-technical audiences.

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Continuously explore emerging AI techniques and tools to improve analytical workflows and accelerate the development of new capabilities.

Requirements:

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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.

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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.

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System architecture: Experience designing scalable, end-to-end solutions that integrate data platforms, APIs, services, AI components, and user workflows.

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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.

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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.

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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.

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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.

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

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

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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.

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

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

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

Benefits:

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

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

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

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

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

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

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

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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. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1