Data Engineer I

General Introduction to Business Unit

At Booking.com, data drives our decisions, technology is at our core, and innovation is everywhere. Finance sits at the centre of that scale. The FP&A Data Engineering team, part of FP&A Tech within Finance, builds and operates the data platforms, pipelines, and data products that Financial Planning & Analysis depends on—from month-end close and regulatory reporting through to planning, forecasting, and self-service analytics. The work is auditable, high-visibility, and consumed directly by Finance leadership and Booking Holdings.

Role Overview

• Own the end-to-end design, delivery, and operation of moderately complex data solutions within the FP&A Data Engineering team.
• Build, deploy, and monitor production-grade data pipelines independently, translating finance business requirements into robust tech designs.
• Connect disparate operational datasets across our SOx-compliant and central company data platforms into well-managed, unified data products.
• Support critical month-end close, statutory/external reporting, forecasting, and self-service analytics through high-quality data modeling (Data Vault and Kimball).
• The FP&A Data Engineering team operates within FP&A Tech in the Finance Business Unit, building and maintaining the core data infrastructure that powers all financial planning, reporting, and analytics across Booking.com and Booking Holdings.
• In this role, you will collaborate closely with FP&A Analysts, Accounting, Tax, Audit, Financial Systems, and Group Reporting teams, as well as central technology and product teams.

Key Job Responsibilities and Duties

• Build and Own Data Solutions:
  • Design, develop, test, deploy, and monitor moderately complex data pipelines and data models in production independently.
  • Model finance data across data warehouse layers using Data Vault and dimensional (Kimball) modelling techniques.
  • Write transformations in SQL and Python, orchestrate workflows (Airflow/Dagster), and manage infrastructure as code via Git and automated CI/CD pipelines.
  • Connect disparate datasets into well-managed, unified data products while contributing to platform migration, semantic layers, and automation.
• Data Quality, Governance and SOx:
  • Own data quality, validation, reconciliation, automated failure detection, and schema validation.
  • Deliver changes to SOx-relevant data assets following change management and access control requirements.
• Operations and Incident Management:
  • Support recurring finance cycles (month-end close, statutory/external reporting) within close-cycle timelines.
  • Participate in operational support rotas, monitor SLIs/SLOs, investigate incidents, and lead postmortems to implement permanent fixes.
• Collaboration and Mentoring:
  • Translate finance business requirements into technical designs and collaborate across central tech and product teams.
  • Help and support less experienced members of the team with their craft.

Role Qualifications and Requirements

• Mandatory / Required:
  • 1–3 years of relevant professional experience as a Data Engineer or similar data-focused engineering role.
  • BSc or higher in Computer Science, Engineering, Information Systems, or a related field (or equivalent work experience).
  • Proven working knowledge of Python and strong SQL (including query performance tuning on large datasets).
  • Hands-on experience with cloud data platforms (Snowflake, AWS, S3) and workflow orchestration (Dagster or Airflow).
  • Experience with ETL/ELT pipelines, dimensional modelling (Kimball), Git (GitHub/GitLab), and CI/CD pipelines.
  • Strong data quality mindset (testing, validation, reconciliation) and ability to work independently in an Agile (Scrum/Kanban) environment.
  • Excellent written and verbal English communication skills.
• Desirable / Nice to Have:
  • Experience with dbt, Data Vault modelling, and Terraform (infrastructure as code).
  • Experience working in SOx-compliant or regulated reporting environments.
  • Exposure to distributed processing (Spark/PySpark) or event-based streaming (Kafka/Flink).
  • Familiarity with data governance tooling (Collibra, Immuta) and AI-assisted engineering tools.