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Agentic Analytics Engineer
Уникальная позиция на острие технологий (Agentic AI) в известной компании. Высокий потенциал влияния на бизнес и возможность формировать стандарты новой дисциплины в аналитике.
Сложность вакансии
Роль требует редкого сочетания навыков: глубокой экспертизы в Data Engineering (SQL, dbt, Snowflake) и практического опыта разработки ИИ-агентов на Python. Высокий уровень ответственности (Senior+) и необходимость работать в условиях неопределенности повышают сложность.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиций уровня Senior+ в сфере AI/Data в США (Майами) рыночные показатели значительно выше средних по IT-сектору. Ожидаемый диапазон составляет $170,000–$230,000 в год плюс опционы.
Сопроводительное письмо
I am writing to express my strong interest in the Senior+ Agentic Analytics Engineer position at Opendoor. With over five years of experience at the intersection of data engineering and analytics, I have consistently focused on moving beyond reactive reporting toward building proactive, automated systems. Your "Default to AI" philosophy resonates deeply with my professional conviction that the next generation of analytics lies in autonomous agents that not only surface insights but recommend concrete business actions.
In my previous roles, I have specialized in architecting complex data models using SQL and dbt, while leveraging Python to build automation workflows that bridge the gap between raw data and executive decision-making. I have hands-on experience with LLM integration and prompt engineering, which I believe is critical for developing the natural language interfaces and semantic layers described in your vision. I am particularly excited about the opportunity to join a team that prioritizes shipping impactful AI products over maintaining stale dashboards.
Opendoor’s mission to revolutionize homeownership is inspiring, and I am eager to apply my technical foundation in Snowflake and AI agent development to help scale your Agentic Analytics team. I am a builder at heart who thrives in ambiguous environments, and I look forward to the possibility of contributing to a team that is defining the future of AI-driven business intelligence.
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Откликнитесь в opendoor уже сейчас
Присоединяйтесь к команде Opendoor и станьте архитектором будущего аналитики, где ИИ-агенты заменяют рутинные дашборды!
Описание вакансии
About the Team
As a Senior+ Agentic Analytics Engineer at Opendoor, you will join the Data organization and help build something that doesn't exist at most companies: an Agentic Analytics team. This is a high-impact IC role at the intersection of data science, data engineering, and AI.
Opendoor has adopted a "Default to AI" philosophy—if AI can handle it, AI should handle it. We're not theorizing about AI transformation; we're shipping it.
Why This Role
Most analytics teams are stuck in reactive mode—fielding ad-hoc requests and building dashboards that go stale. We're building something different: AI systems that proactively tell leaders what they should focus on, why it matters, and what to do about it.
If you want to define what analytics looks like in an AI-first company, this is it.
What You'll Do
Build Proactive Intelligence Systems
- Design and deploy AI agents that monitor business performance and surface actionable insights without being asked
- Architect systems that shift analytics from "answering questions" to "anticipating needs"
- Create intelligent alerting that distinguishes signal from noise and recommends specific actions
Create AI-Powered Analytics Tools
- Develop natural language interfaces that let business leaders query data conversationally
- Build semantic layers that translate business concepts into data structures optimized for LLM reasoning
- Ship internal tools that transform how Opendoor makes decisions at every level
Own the Analytics-to-Action Pipeline
- Design data models that support automated analysis, pattern detection, and AI agent interactions
- Build workflows that connect insight generation to recommended actions
- Establish feedback loops that make our AI systems smarter over time
Drive Leverage Across the Business
- Identify high-value opportunities where AI can augment human decision-making
- Implement end-to-end solutions using Claude, custom agents, and modern AI tooling
- Measure impact rigorously—we care about decisions influenced, not dashboards built
We're Looking for Teammates Who Have
Technical Foundation
- 5+ years of experience in analytics, data science, or data engineering, with increasing scope and ownership
- Expert-level SQL with demonstrated ability to design complex data models and optimize for both human and AI consumption
- Strong Python proficiency for data engineering, automation, and AI agent development
- Experience with modern data warehouses (Snowflake, BigQuery, or similar) and the broader data stack (dbt, Airflow, etc.)
AI & Automation Expertise
- Hands-on experience building and deploying AI agents or LLM-powered applications in production
- Deep understanding of LLM capabilities, limitations, and prompt engineering best practices
- Experience architecting semantic layers or natural language interfaces for data access
- Proven ability to identify high-ROI automation opportunities and ship end-to-end solutions
Analytics & Business Acumen
- Track record of driving measurable business impact through analytics—not just producing analyses, but influencing decisions
- Strong analytical intuition—you know what questions matter before they're asked
- Experience translating ambiguous, cross-functional business problems into technical solutions
- History of operating autonomously and setting direction, not just executing on defined projects
Leadership & Influence
- Experience mentoring other analysts or engineers, formally or informally
- Demonstrated ability to influence senior stakeholders and drive alignment across teams
- Comfort operating in ambiguity and shaping strategy, not just responding to it
- Track record of building systems and processes that scale beyond yourself
Builder Mindset
- You ship products, not just dashboards
- You'd rather build something useful in a week than something perfect in a quarter
- You measure success by business impact, not technical complexity
- You're energized by creating leverage—building once so others benefit forever
Bonus Points If You Have
- Experience in real estate, fintech, or marketplace businesses
- Background in marketing analytics, sales operations, product analytics, or pricing/economics
- History of building tools or platforms that replaced recurring analyst work
- Experience leading technical projects or small teams
At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Homeownership matters. It's how people build wealth, stability, and community. It's how families put down roots, how neighborhoods strengthen, how the future gets built. We're building the modern system of homeownership giving people the freedom to buy and sell on their own terms. We’ve built an end-to-end online experience that has already helped thousands of people and we’re just getting started.
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Навыки
- SQL
- Python
- Snowflake
- BigQuery
- dbt
- Airflow
- LLM
- Prompt Engineering
- Data Modeling
- Claude
Возможные вопросы на собеседовании
Проверка опыта работы с LLM в реальных условиях и понимания их ограничений.
Расскажите о самом сложном случае внедрения LLM или ИИ-агента в продакшн: с какими проблемами галлюцинаций или точности данных вы столкнулись и как их решили?
Вакансия делает упор на переход от дашбордов к проактивным системам. Важно понять, как кандидат проектирует такие системы.
Как бы вы спроектировали семантический слой, который позволяет бизнес-пользователям запрашивать сложные метрики через естественный язык, минимизируя риск неверной интерпретации SQL-кода моделью?
Оценка навыков моделирования данных специально для нужд ИИ.
В чем, по вашему мнению, заключается ключевое различие в проектировании архитектуры данных для потребления человеком (BI) и для потребления ИИ-агентами?
Проверка умения приоритизировать задачи на основе бизнес-ценности.
Опишите ситуацию, когда вам пришлось выбирать между созданием 'идеального' технического решения и быстрым запуском продукта. Как вы оценивали ROI в этом случае?
Оценка лидерских качеств и умения влиять на стейкхолдеров.
Как вы убеждаете руководство доверять решениям, принятым ИИ-агентом, особенно в критически важных для бизнеса процессах, таких как ценообразование или инвестиции?
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