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Чёткая зона ответственности на уровне Principal, современные инструменты для работы с данными и влияние на несколько команд делают вакансию сильной для опытного специалиста, хотя ритейл менее ориентирован на технологии, чем продуктовая IT-компания.
Современный стек
Чёткая зона ответственности
Сильный соцпакет
Гибридный формат
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Текст:
Hirify AI/Оригинал
TL;DR
Principal Data Engineer (Python/SQL/Cloud Data Platforms): Designing and evolving scalable data platforms and production-grade pipelines with an accent on distributed processing, cloud-native architecture, data quality, and platform reliability. Focus on leading cross-team architecture decisions, building observable and cost-efficient systems, and raising engineering standards across multiple squads.
Location: Hybrid role based in Southampton or London, England, with around 20% of Engineering time involving in-person collaboration.
Company
hirify.global is a home improvement retailer operating brands including B&Q, Screwfix, Brico Depot, Castorama and Koctas, with more than 74,000 people.
What you will do
Define and drive the data platform and data engineering strategy in line with Group Technology objectives.
Lead architecture decisions and establish scalable standards and design patterns for data pipelines, storage and processing.
Design and deliver scalable, resilient, observable and cost-efficient data platforms and production pipelines.
Partner with Architecture and Product teams on long-term platform roadmaps and capabilities.
Strengthen data quality, governance, security and reusable data products while promoting CI/CD, testing, DevOps, DataOps and platform automation.
Lead multi-team initiatives, influence senior stakeholders, coach engineers, support hiring and evaluate emerging technologies.
Requirements
Deep expertise in distributed data processing, cloud-native systems and modern data architecture.
Proven experience delivering large-scale data platforms and production-grade pipelines.
Strong Python and SQL skills, with hands-on experience in Spark, PySpark or Scala.
Experience with dbt for data modelling and transformation.
Strong knowledge of GCP, Azure or AWS, including services such as Databricks, BigQuery or Azure Data Factory.
Strong understanding of DevOps and DataOps practices and tools, including CI/CD, Git, Terraform, Docker and Kubernetes.
Experience with data quality tools and testing frameworks such as Great Expectations.
Deep understanding of batch, streaming and event-driven architecture patterns, including resilient, scalable and observable system design and performance and cost optimisation.
Track record of setting engineering standards, driving continuous improvement, influencing strategic and investment decisions, working through ambiguity, and coaching engineers.
Culture & Benefits
Hybrid working model balancing home working with office and offsite collaboration.
Inclusive environment focused on experimentation, new ideas, diversity and equal treatment.
Private healthcare, pension scheme, life assurance and competitive bonus scheme.
25 days’ holiday plus bank holidays, staff discounts and share incentive and share-save plans.
Opportunities for career development, mentoring and participation in a wider Data Engineering community.
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