Data Model Architectural Lead

2 semanas atrás

Porto, Porto, Portugal Hostelworld Group Tempo inteiro

About Us

Hostelworld Group, the global hostel-focused online booking platform, inspires adventurous minds to meet the world and come back with life-changing stories to tell. Our customers are not your average tourists, they crave cultural connection and unique experiences that we make possible by providing an unbeatable selection of hostels in unmissable locations – all in the palm of their hand.

The social nature and community feel of hostels and their environment enable travellers to embrace journeys of discovery, adventure and meaning. We have more than 13 million reviews across 17,800 hostels in over 179 countries, making us the leading online hub for social travel. The website operates in 19 different languages and our mobile app in 13 languages.

Founded in 1999 and headquartered in Dublin, Hostelworld has a growing team of 230 people within Technology, Product, Global Markets, HR, Finance & Legal and Marketing Teams across our Dublin, London, Porto, Shanghai and Sydney offices. We're listed on the London Stock Exchange and Dublin Euronext.

Location

This role is based in Portugal. We have an office hub in Porto available for those who prefer a hybrid model where you can spend time with colleagues in-person. You'll need to be able to commute to our office hub as required from time to time.

Who You'll Work With

The Analytics and Insights team at Hostelworld comprises of 20+ people who provide insights across various growth units including paid marketing, customer analytics and app analytics to support decision-making at C and senior management level.

As Data Model Architectural Lead, you'll play a crucial role in transforming raw data into a well-organized, scalable and insightful data model that empowers our analytics and business intelligence efforts. You'll work closely with data analysts, data scientists and other stakeholders to ensure the delivery of high-quality, reliable data that drives decision-making across the organization.

What You'll Do

  • Data Tooling Team Contribution: Collaborate with the Data Tooling/Data Platform team to design and implement scalable and efficient data models, pipelines and tools that empower our analytics and data science teams.
  • Data Modelling: Design, develop and maintain robust and scalable data models using dbt to support analytical and reporting needs.
  • SQL & Python Development: Write and optimize SQL queries, and develop Python scripts to transform raw data into refined datasets and automate data processing tasks.
  • ETL/ELT Pipelines: Build and manage ETL/ELT processes using a combination of dbt and Python to ensure efficient data flow from various sources into our data warehouse.
  • API Integrations: Develop and maintain API connections to ingest data from various external sources, ensuring seamless integration into the data pipeline.
  • Data Science Support: Collaborate with data scientists to develop, deploy and maintain data pipelines that support machine learning models and other advanced analytics initiatives.
  • Collaboration: Work closely with data analysts, data scientists and business stakeholders to understand their data needs and deliver solutions that meet those needs.
  • Data Quality: Implement and monitor data quality checks within dbt and Python scripts to ensure the accuracy, consistency and reliability of our data.
  • Documentation: Create and maintain comprehensive documentation of data models, transformation logic, Python scripts and data lineage.
  • Performance Optimisation: Continuously optimise the performance of data transformations and queries to improve the efficiency of our data processing workflows.
  • Best Practices: Advocate and implement best practices in data modelling, version control and dbt usage to maintain a clean and efficient analytics environment.
  • Training & Support: Provide guidance and support to team members in the use of dbt, Python, SQL and best practices for data science integrations.

What We're Looking For

  • Strong proficiency in SQL, with a deep understanding of database design and data warehousing concepts.

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