Senior Data Analytics Engineer

TL;DR

Senior Data Analytics Engineer: Building and scaling the Intelligence Platform leveraging semantic layer, RAG, and MCP for agentic self-service insights and autonomous intelligence with an accent on reliable data modeling and high-quality data software. Focus on guiding product strategy through deep-dive analysis, KPI definition, and supporting ML workflows with robust pipelines.

Location: On-site position requiring 4 days a week (Monday-Thursday) in San Francisco HQ

Company

hirify.global unifies people, spaces, and communications in one secure, integrated workplace management platform trusted by over 16,000 workplaces worldwide.

What you will do

• Develop and scale the Intelligence Platform, including semantic layer and RAG infrastructure for human and agentic workflows.
• Partner with Product teams to drive prioritization, feature development, KPI definition, and data modeling.
• Build and maintain data pipelines and feature sets for machine learning models and data science projects.
• Champion software engineering best practices like version control, CI/CD, and peer reviews.
• Mentor team members on solution design, code quality, and architecture.

Requirements

• 5+ years in Analytics Engineering, Data Science, or Data Engineering in high-growth startups
• Mastery of dbt and SQL for complex, large-scale projects
• Proficient in Python for ML workflows, LLM APIs, and Vector Databases
• Hands-on with Git, CI/CD, and automated data testing
• AI-first mindset with experience leveraging LLMs while maintaining system integrity

Nice to have

• Product analytics experience influencing roadmaps and measuring features
• Agentic workflows and real-time data with Estuary or streaming tools
• Vector DB experience like OpenSearch for RAG

Culture & Benefits

• High standards for code, systems, practices, and people in a top-notch data organization
• Modern data stack: Redshift, Databricks, dbt, Airflow, Omni, Segment, Estuary
• Emphasis on lifelong learning, proactive problem-solving, and emotional maturity
• Collaborative environment partnering with Product, ML, and Data Science teams