Senior AI & Analytics Engineer

Jobgether; United States; Remote; USD 140.000 - 190.000; Vollzeit

Benefits dieser Stelle

Remote work from India

Zusammenfassung

Jobgether is seeking a senior analytics engineer to build scalable data pipelines, define metrics, and deploy AI-powered analytics workflows. You will work on a governed metrics layer and integrate AI components into production systems.

The role emphasizes cross-functional collaboration with product and engineering in a remote, globally distributed environment. Strong SQL/Python and production experience are required.

Qualifikationen

• 6+ years in analytics engineering, data engineering, or applied AI.
• Strong SQL and Python; production-grade data pipelines.
• Experience architecting end-to-end data/AI solutions.
• Experience with B2C/product analytics and KPI derivation.
• Hands-on with at least one LLM-powered app using RAG or AI agents.
• Familiarity with AI governance, monitoring, and safety practices.

Aufgaben

• Build and optimize scalable data pipelines and analytics datasets.
• Develop governed metrics for retention, conversion, and LTV.
• Design and deploy LLM-powered applications and analytics workflows.
• Integrate data platforms, APIs, and AI components into production services.
• Collaborate with product, growth, and engineering stakeholders.

Kenntnisse

Analytics engineering

SQL

Python

System architecture

Production AI development

Agent frameworks

Data platforms & BI tools

AI governance

Conversational analytics

Docker/Kubernetes/CI/CD

Communication

Remote collaboration

Tools

LangGraph

LangChain

Semantic Kernel

CrewAI

AutoGen

OpenAI Agents SDK

Jobbeschreibung

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 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.