Data Engineer

• As a Data Engineer in this area, you will help build the data foundation that powers Messaging & Rewards use cases across channels such as email, push notifications, in-app messaging and emerging channels.
• Your work will focus on creating reliable, scalable and well-governed data pipelines and datasets that enable audience creation, campaign performance measurement, funnel observability, customer engagement analysis, personalization and rewards effectiveness
• You will have the opportunity to work on foundational problems such as event logging, sent and engagement data coverage, cross-channel attribution, pipeline reliability and the modeling of customer and messaging data needed to support both operational decision-making and long-term product and marketing strategy
• As a Data Engineer, you will be in charge of the development, performance, quality, and scaling of our data pipelines, focusing especially on data quality. You will work independently and will also be responsible for making technical decisions within a team
• Rapidly developing next-generation scalable, flexible, and high-performance data pipelines
• Solving issues with data and data pipelines, prioritizing based on customer impact
• End-to-end ownership of data quality in our core datasets and data pipelines
• Experimenting with new tools and technologies, driving innovative engineering solutions to meet business requirements regarding performance, scaling, and data quality
• Providing tools that improve data quality company-wide
• Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise
• Serving as the main point of contact for technical and business stakeholders regarding data engineering issues, such as pipeline failures and data quality concerns
• Acquiring technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation

Benefits

• Health insurance
• Free access to Headspace for you and your loved ones
• Global Employee Assistance Program
• Meditation and Breastfeeding rooms at the office
• Booking Cares - 2 days per year to volunteer and learn
• Life insurance
• Disability insurance
• Pension plan
• Annual paid time off
• Parental leave - 22 weeks
• Grandparent leave - 10 days
• Care leave - 10 days
• Bereavement leave - up to 4 weeks
• Anniversary leave
• Working from Home Furniture and Ergonomic Support
• Working from Abroad - up to 20 days per year
• Discounts & Wallet credits to spend on our products
• Upgrade to Booking.com Genius Level 3
• Friends & Family Booking.com discount vouchers
• Free access to online learning platforms
• Development and mentorship programs to support career growth
• Access to trainings and workshops
• Team development opportunities
• Local discount programs
• Game rooms in offices
• On-site meals, coffee and snacks including vegan options- Excellent communication skills are essential; you are able to work effectively with developers, product managers, ML scientists and ML engineers, and translate requirements into robust technical solutions that enable high-quality data products
• You have experience working with data warehousing and ETL/ELT pipelines, and understand how to model, transform and expose data so it can be trusted and effectively used by analytics, product and machine learning stakeholders
• You have hands-on experience building and operating production-grade data pipelines and data processing systems in cloud and hybrid environments, with a strong focus on scalability, reliability and data quality
• You have 3–5 years of relevant professional experience as a Data Engineer, Software Engineer or in a similar data-focused role, and hold a Master’s degree in a quantitative field
• You have practical experience with modern data and orchestration technologies such as Spark, Kafka, Airflow, Snowflake, DBT, CDC patterns, cloud storage and related data engineering tools
• You have experience designing systems end to end and a solid understanding of architecture fundamentals such as APIs, distributed systems, storage, caching, data modeling and system reliability
• You are able to balance business needs, technical constraints and long-term maintainability when evaluating solutions, and you actively look for opportunities to improve process efficiency, platform quality and engineering standards
• You are comfortable owning data products and services in production, including monitoring, incident response, root cause analysis, documentation and on-call support where required
• Proven knowledge of Python is required; experience with Java or Scala is a plus