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SeniorВ офисеПолная занятость

Senior Data Platform Engineer, ISS - Data Platform Engineering

Оценка ИИ

Отличная позиция в престижной компании с современным стеком технологий и сильной инженерной культурой. Работа в Праге в офисе мирового лидера индустрии хранения данных предлагает отличные возможности для профессионального роста.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует глубоких знаний в области Big Data (Iceberg, Trino, Kafka) и опыта построения сложных платформ данных с нуля. Высокая планка ответственности за архитектуру и производительность в крупной технологической компании.

Анализ зарплаты

Медиана5 500 €
Рынок4 500 € – 7 000 €
Оценка ИИ

Предлагаемая позиция Senior уровня в международной компании Pure Storage в Праге обычно предполагает зарплату выше среднего по рынку Чехии, учитывая высокие требования к экспертизе в Big Data.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Data Platform Engineer position at Pure Storage. With over 6 years of experience in building scalable data infrastructure and a deep technical background in Python, SQL, and the modern data stack, I am excited about the opportunity to help shape the Data Platform Engineering capability within your Infrastructure Shared Services organization.

In my previous roles, I have successfully implemented complex ETL processes and managed large-scale data lakehouse architectures. I have extensive experience working with Apache Iceberg, Trino, and Airflow, which aligns perfectly with your goal of creating a unified data plane. I am particularly drawn to Pure Storage's vision of removing friction for developers and providing a consistent, secure data service across the entire organization.

I am a proactive problem-solver with a growth mindset, and I thrive in collaborative environments where innovation is celebrated. I am eager to bring my expertise in multi-engine environments and ingestion patterns to the team in Prague and contribute to the continued success of Pure Storage as a leader in the data storage industry.

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Откликнитесь в purestorage уже сейчас

Присоединяйтесь к Pure Storage в Праге и станьте архитектором современной платформы данных на базе Iceberg и Trino!

Описание вакансии

We’re in an unbelievably exciting area of tech and are fundamentally reshaping the data storage industry. Here, you lead with innovative thinking, grow along with us, and join the smartest team in the industry.

This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us.

THE ROLE

We’re building a new capability inside Platform Engineering, within our Infrastructure Shared Services (ISS) organisation: Data Platform Engineering. Our goal is to create a platform that removes friction, providing a consistent way to build, operate, and evolve data-driven services with clear ownership, guardrails, and a strong developer experience. Everpure’s own platform thinking is anchored in the idea of a unified data plane - a single fabric that connects data across locations and delivers consistent, secure data services across protocols and applications.

In this role, you’ll build and operate a trusted, scalable, and intelligent data platform that empowers teams to make faster data-driven decisions while shaping a culture of ownership and innovation to meet the needs of an ever-changing business and technology landscape.

WHAT YOU'LL DO

  • Design, build, and operate the core data platform services that enable teams to ingest, model, query, and publish data seamlessly.
  • Architect a multi-engine environment (e.g., Trino/Starburst, Dremio, ClickHouse, Postgres/pgLake) with an Iceberg-based lakehouse, prioritizing interoperability, operability, and security.
  • Drive platform evolution through experimentation, measuring performance, reliability, and cost trade-offs to standardize high-impact solutions.
  • Establish durable corporate knowledge by developing strong defaults, comprehensive documentation, and intuitive self-service interfaces.
  • Manage core lakehouse components, including table layout conventions, catalogue integration, and critical lifecycle maintenance patterns such as compaction and optimization.
  • Implement robust ingestion patterns for both batch and streaming data, ensuring clear SLAs and highly observable failure modes.
  • Enable multi-engine access by configuring connectors and catalog permissions while maintaining strict guardrails for a consistent user experience.

WHAT YOU BRING

  • 6+ years of experience in the design and development of data pipeline automation, specifically extracting data from API-based sources.
  • Technical expertise in developing complex automation frameworks, data modeling, and ETL processes using SQL, Python, DBT, Apache Airflow, or similar languages.
  • Full-stack proficiency across the data stack, with deep knowledge of Python, SQL, and ETL methodologies.
  • Hands-on familiarity with the big data ecosystem, including technologies such as Trino, Kafka, and Iceberg.
  • A growth mindset characterized by curiosity and a proactive approach to solving complex architectural problems.
  • Strong collaborative communication skills, with a proven ability to align and work effectively across diverse technical stakeholder groups.
  • We are primarily an in-office environment and therefore, you will be expected to work from the Prague office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave.

#LIONSITE

WHAT YOU CAN EXPECT FROM US:

  • Innovation: We celebrate those who think critically, like a challenge, and aspire to be trailblazers.
  • Growth: We give you the space and support to grow along with us and to contribute to something meaningful. We have been named Fortune's Best Workplaces in Technology™, Fortune's Best Workplaces in the Bay Area™, and certified as a Great Place to Work®!
  • Team: We build each other up and set aside ego for the greater good.

And because we understand the value of bringing your full and best self to work, we offer a variety of perks to manage a healthy balance, including flexible time off, wellness resources, and company-sponsored team events. Check out purebenefits.com for more information.

ACCOMMODATIONS AND ACCESSIBILITY:

Candidates with disabilities may request accommodations for all aspects of our hiring process. For more on this, contact us at TA-Ops@purestorage.com if you’re invited to an interview.

OUR COMMITMENT TO A STRONG AND INCLUSIVE TEAM:

We’re forging a future where everyone finds their rightful place and where every voice matters. Where uniqueness isn’t just accepted but embraced. That’s why we are committed to fostering the growth and development of every person, cultivating a sense of community through our Employee Resource Groups and advocating for inclusive leadership.

Everpure is proud to be an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other characteristic legally protected by the laws of the jurisdiction in which you are being considered for hire.

Join us and bring your best.

Bring your bold.

Pure and simple.

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Навыки

  • Python
  • SQL
  • dbt
  • Apache Airflow
  • Trino
  • Apache Kafka
  • Apache Iceberg
  • ClickHouse
  • PostgreSQL
  • Dremio
  • ETL
  • Data Modeling

Возможные вопросы на собеседовании

Проверка опыта работы с ключевой технологией стека компании.

Опишите ваш опыт оптимизации производительности в Apache Iceberg. Какие стратегии компакции вы использовали?

Оценка навыков проектирования сложных систем.

Как бы вы спроектировали систему доступа к данным в мульти-движковой среде (Trino, ClickHouse, Postgres), обеспечивая при этом единые правила безопасности?

Проверка навыков работы с потоковыми данными.

С какими основными проблемами вы сталкивались при реализации паттернов инъекции данных из Kafka в Lakehouse и как их решали?

Оценка понимания жизненного цикла разработки данных.

Как вы подходите к обеспечению наблюдаемости (observability) и соблюдению SLA в сложных ETL-процессах на базе Airflow?

Проверка соответствия культуре компании и подходу к платформенному мышлению.

Что для вас означает 'хороший опыт разработчика' (Developer Experience) применительно к платформе данных?

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