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SeniorУдалённоПолная занятость

Senior Data Engineer

Оценка ИИ

Отличная вакансия в быстрорастущем стартапе с современным стеком (Snowflake, dbt, AI). Высокая степень ответственности и возможность влиять на архитектуру делают её очень привлекательной для опытных инженеров.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена ролью 'player-coach', требующей не только глубоких знаний Snowflake и Python, но и опыта построения LLM-стратегий и RAG-систем. От кандидата ожидается более 7 лет опыта и способность проектировать архитектуру с нуля.

Анализ зарплаты

Медиана190 000 $
Рынок160 000 $ – 230 000 $
Оценка ИИ

В объявлении зарплата не указана, но для позиции Senior/Staff Data Engineer в США на удаленной основе рыночный диапазон составляет $160,000 – $220,000 в год. Учитывая стадию роста компании и сложность задач (AI/LLM), можно ожидать предложение в верхней части этого диапазона плюс опционы.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Data Engineer position at Alvys. With over 7 years of experience in building mission-critical data platforms and a deep expertise in the Snowflake ecosystem, I am excited by the opportunity to architect a next-generation data strategy for the $800 billion US transportation logistics industry.

In my previous roles, I have successfully led the transition to Snowflake-centric architectures and implemented complex ELT pipelines using dbt and Fivetran. I am particularly drawn to Alvys' vision of integrating LLM-based data products and Snowflake Cortex into the core TMS platform. My experience in operationalizing ML models and designing RAG-based applications aligns perfectly with your goal of turning fragmented logistics data into actionable AI-driven products.

I am a 'player-coach' who thrives in high-visibility roles where technical excellence meets strategic leadership. I look forward to the possibility of bringing my pragmatism and ownership mindset to your engineering team to help Alvys remain the fastest-growing TMS in history.

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Описание вакансии

Industry Insight

Transportation logistics is the foundational industry that powers everything we rely on daily, yet it remains a complex and fragmented domain ripe for technological revolution. The US transportation logistics industry alone is an $800 billion market.

Alvys is not just participating in this change; we are driving it as the fastest-growing Transportation Management System (TMS) in history, uniquely positioned to truly transform this essential sector.

Your role will be at the forefront of automating and standardizing a sector that moves trillions of dollars' worth of goods annually, predominantly by truck, but still lacks modern tools and solutions. Your leadership will help bridge this gap, bringing innovative technologies to a traditionally underserved and mission-critical industry.

About Alvys

Alvys is the fastest-growing Transportation Management System (TMS) in history, uniquely positioned to dominate and truly revolutionize the $800 billion US transportation logistics industry.

We're not just iterating on old tools; we're building a world-class, multi-tenant SaaS platform from the ground up to automate, standardize, and transform this essential sector. Join us to apply your technical vision to one of the economy's most mission-critical and underserved markets, making a massive, tangible impact on the movement of global goods.

This is where industry experience meets cutting-edge technology to create the undisputed market winner, and you will be a core part of that journey.

About the Role

As a Staff Data Engineer, you will be the primary architect of our data ecosystem. Alvys is shifting toward a Snowflake-centric data architecture to power our next generation of logistics intelligence. You aren't just building pipelines; you are defining our LLM-based data strategy, enabling large-scale ML model deployment, and leveraging Snowflake Cortex to turn fragmented logistics data into actionable AI-driven products.

This is a high-visibility, 'player-coach' position. You will work closely with leadership to ensure our data infrastructure supports real-time operational needs today while scaling for the AI-first logistics world of 2026 and beyond.

Our Tech Stack

  • Data Platform: Snowflake (Warehouse, Cortex AI/ML, Semantic Models, LLM Agents)
  • Data Pipelines: dbt, Fivetran, Reverse ETL (Census)
  • AI/ML: Snowflake Cortex, OpenAI, Azure Cognitive
  • Backend/Infra: .NET/C#, Python, Azure Ecosystem, GitHub Actions

Key Responsibilities

  • Define and implement the strategy for LLM-based data products, including data preparation, semantic layer design, and embedding pipelines for RAG-based applications.
  • Own the design and evolution of our Snowflake architecture. Lead the implementation of Snowflake Cortex for AI/ML workloads, including semantic models and LLM agents.
  • Design and maintain reliable, performant ELT/Reverse ETL pipelines. Ensure our multi-tenant SaaS data remains isolated, secure, and performant at scale.
  • Build and operationalize pipelines for large-scale ML and LLM model development, moving from POC to production-grade deployment within the Snowflake perimeter.
  • Think holistically about the data estate. Reduce complexity through modularity and well-defined service boundaries (e.g., Medallion Architecture).
  • Serve as the go-to expert for emerging data trends. Mentor engineers and partner with Product and Design to translate complex business logic into automated deliverables.

The Experience You Have

  • 7+ years of experience in Data Engineering with a proven track record of delivering mission-critical data platforms.
  • Deep proficiency in Snowflake (Data Modeling, Query Optimization, Security, and Cortex AI functions).
  • Expert-level skill in SQL and Python. Extensive experience with dbt and modern orchestration (Airflow/Dagster).
  • Demonstrated experience building pipelines for ML or LLM-based applications, including feature engineering and model deployment.
  • Competency in Azure (preferred) or other major cloud providers, including CI/CD and infrastructure-as-code principles.
  • Experience driving cross-team consensus on architectural decisions and mentoring junior/mid-level engineers.

The Way You Work

  • Ownership: You take end-to-end responsibility. If it’s in production, it’s yours—from the initial schema design to the final dashboard latency.
  • Pragmatism: You believe technical debt is a strategic choice. You choose solutions that scale and solve real problems over theoretical "perfection."
  • Simplicity: You prioritize modularity and clean design to reduce cognitive load in a complex logistics domain.
  • Transparency: You democratize data. You ensure insights are accessible across the organization and participate in constructive feedback loops.

Equal Employment Opportunity

Alvys is an equal opportunity employer and as such, we do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other characteristic protected by applicable laws.

We are dedicated to growing a diverse team of highly talented people. We’re dedicated to building a workplace where we give each other the strategies, support, and space we each need to thrive—believing in and bringing out the best of everyone.

If you require any accommodations during the recruitment process, whether it be alternate forms of material, accessible meeting rooms, etc., please let us know and we will work with you to meet your needs.

For information about Alvys's privacy practices, see ourPrivacy Policy.

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Создайте идеальное резюме с помощью ИИ-агента

Создайте идеальное резюме с помощью ИИ-агента

Навыки

  • Snowflake
  • Python
  • SQL
  • dbt
  • Fivetran
  • Azure
  • LLM
  • Machine Learning
  • ETL
  • Reverse ETL
  • Airflow
  • Dagster
  • C++
  • .NET
  • GitHub Actions

Возможные вопросы на собеседовании

Вакансия делает упор на Snowflake Cortex и LLM. Важно понять, как кандидат будет интегрировать ИИ в хранилище данных.

Как бы вы спроектировали пайплайн для RAG-приложения, используя Snowflake Cortex и семантические модели для логистических данных?

Роль предполагает работу с мультиарендной (multi-tenant) SaaS-платформой. Безопасность данных критична.

Какие стратегии изоляции данных в Snowflake вы считаете наиболее эффективными для обеспечения безопасности в мультиарендной SaaS-архитектуре?

Упоминается использование dbt и Medallion Architecture. Проверка навыков структурирования данных.

Опишите ваш опыт внедрения Medallion Architecture с использованием dbt. Как вы разделяете логику между слоями Bronze, Silver и Gold?

Позиция требует навыков наставничества и принятия архитектурных решений.

Расскажите о случае, когда вам пришлось убеждать команду или руководство принять непопулярное, но стратегически верное архитектурное решение.

Логистические данные часто фрагментированы. Важно умение работать с качеством данных.

Как вы организуете мониторинг качества данных и обработку ошибок в реальном времени при использовании Fivetran и Reverse ETL?

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