We’re looking for an Analytics Engineer who excels at building trusted analytical and semantic foundations and is excited to extend that work into LLM-powered and agentic analytics systems. You will design the data models, metrics, and semantic layers that bridge raw data and business logic—making them consumable by both people and AI.

Over time, you’ll grow beyond classic analytics engineering to help shape analytics agents that retrieve context, reason over metrics, and generate insights through multi-step workflows. This will allow business users to ask complex questions in natural language and receive accurate, decision-ready answers grounded in production data. Your work will influence how analytics, AI agents, and business teams interact with data at scale.

Core Responsibilities

• Design and maintain robust analytical data models and transformations that encode business logic, KPIs, and decision-critical metrics.

• Build and own semantic layers (metrics, dimensions, definitions, lineage) that translate raw data into clear, trusted business concepts.

• Partner with business and product stakeholders to ensure metrics are accurate, interpretable, and aligned with real-world decision-making.

• Integrate semantic layers with LLM-powered analytics experiences, enabling natural-language querying and AI-assisted insight generation.

• Develop and maintain context and metadata retrieval (schemas, metric definitions, examples, documentation) to ground LLM reasoning in trusted analytics foundations.

• Collaborate with AI and product teams to extend semantic analytics into advanced analytical workflows, including multi-step analysis and agent-assisted exploration.

Required Qualifications

• 4+ years of experience building analytical data models, metrics, and transformations for real business use cases.

• Advanced proficiency in SQL and hands-on experience with modern transformation tools (e.g., dbt or equivalent); familiarity with SparkSQL/Databricks is a plus.

• Strong understanding of dimensional modeling, analytical schemas, and modern data warehouse architectures.

• Experience building or managing semantic layers, metrics frameworks, or BI-ready analytical models.

• Proven ability to translate business questions into clear, maintainable data models and semantic abstractions.

• Professional working proficiency in English, with the ability to explain technical concepts clearly to international teams.

Preferred Qualifications

• Hands-on exposure to LLM-powered analytics, such as natural-language querying, AI-assisted insights, or conversational analytics interfaces.

• Familiarity with context engineering (CE) concepts for analytics and agent use cases (metadata & schema retrieval, metric grounding, prompt inputs, example selection).

• Experience building or collaborating on analytics agents, including multi-step workflows, tool-based reasoning, or agent-driven exploration over structured data.

• Working proficiency in Python and interest in building analytics or semantic-layer-driven systems that integrate LLMs and agents.