Data Analytics Engineer I - Treasury Data Science & Analytics

About Booking Holdings

Booking Holdings (NASDAQ:BKNG) is the world's leading provider of online travel and related services, provided to consumers and local partners in more than 220 countries and territories through five primary consumer-facing brands: Booking.com, Priceline, Agoda, KAYAK and OpenTable. The mission of Booking Holdings is to make it easier for everyone to experience the world. For more information, visit BookingHoldings.com and follow us on X (formerly known as Twitter) @BookingHoldings.

About the team:

The Treasury Data Science & Analytics team develops trusted, scalable and reusable analytical products that enable cash flow forecasting, liquidity optimization, risk modelling, reporting and self-service analytics for the Booking Holdings Group Treasury team.

Our team brings together data analysts and data scientists to develop analytical solutions that support business decisions and improve operational processes. We connect data across business units and systems, develop analytical tools and modernize existing solutions. We continuously improve the data foundations, tooling and automation that support these capabilities.

We work closely with business and technology teams to translate requirements into practical solutions. Our focus is on consistent definitions, reusable data models and reliable production workflows that support daily operations and new analytical use cases.

Role Description:

As a Data Analytics Engineer I in the Treasury Data Science & Analytics team, you will contribute to the design, development and maintenance of analytical data models, processing pipelines and reusable data products.

This is a hands-on data engineering role which sits at the intersection of analytics and engineering with a strong focus on migrating and modernising the team’s analytical frameworks. You will take ownership of defined migration deliverables and help establish consistent practices for developing, deploying, scheduling, monitoring and maintaining pipelines. As the first and only Analytical Engineer on the team, you should enjoy working independently while partnering closely with finance business stakeholders.

Working with data scientists and analysts, you will build reusable components, strengthen data quality controls, improve the reliability and own the maintenance of existing solutions. You will work within engineering standards, with guidance on complex design decisions.

Key Job Responsibilities and Duties:

- Take ownership of workflow migration and modernisation
- Build and optimize analytical data assets and pipelines using dbt and PySpark, improving execution time and resource efficiency. Integrate data from multiple sources and prepare curated datasets with consistent business definitions and clearly defined granularity, historical coverage and schedules.
- Help establish consistent workflow management across the team, covering scheduling, dependencies, retries, backfills, health monitoring and alerting. Take responsibility for assigned workflows and support incident investigation and resolution.
- Implement automated data quality controls, including recency, completeness, uniqueness, reconciliation, schema-change detection and validation of critical business metrics.
- Develop and maintain data processing and integration components for analytical applications and operational automation, adapting solutions as source systems and business requirements evolve.
- Build and maintain Snowflake semantic views and implement agreed metric definitions to support self-service and AI-assisted analytics. Support data scientists and analysts with the data models behind dashboards and reports, helping validate data consistency and metric accuracy.
- Establish data governance and access-control requirements, maintaining clear data definitions, lineage and technical documentation.

Role Qualifications and Requirements

- 1–3 years of relevant experience in Data Analytics Engineering, Analytics Engineering, Data Engineering, Data Warehousing or a related data or software role.
- Bachelor’s degree or higher in Computer Science or a related field, or equivalent professional experience.
- Strong SQL skills, including combining datasets, implementing transformation logic and investigating discrepancies.
- Programming experience in Python, with the ability to write clear, maintainable and reusable code.
- Practical experience with analytical data modelling, including data granularity, relationships, aggregation and historical data.
- Experience building data transformation pipelines using dbt or a comparable framework.
- Experience working with relational databases or cloud data warehouses; familiarity with Snowflake is beneficial.
- Experience supporting scheduled workflows and using logs, monitoring and alerts to investigate issues.
- Experience implementing automated data quality checks and validating outputs against source data.
- Experience using Git, code reviews, automated testing and existing CI/CD pipelines.
- Experience working with cloud services for data processing or storage, and practical experience deploying or troubleshooting containerized workloads within existing Docker/Kubernetes environments.
- Experience exploring and visualizing data and explaining findings to business stakeholders.
- Understanding of data governance and security fundamentals, including documentation, traceability and access controls.
- Stakeholder-management and communication skills, including the ability to explain complex technical decisions and trade-offs.

Key Skills

Core skills

- SQL-based data transformation using dbt or a comparable framework; Python programming
- Analytical data modelling and warehousing, including dimensional modelling
- Data pipeline design, API integration, workflow migration and orchestration
- Software engineering practices: Git, code reviews, automated testing and CI/CD
- Data quality controls and reconciliation
- Production monitoring, troubleshooting, performance and cost optimisation
- Practical experience with cloud services and containerized workloads using Docker/Kubernetes
- Data governance, access controls and technical documentation
- Data exploration, visualization and stakeholder communication
- Basic understanding of statistical modelling and machine learning concepts

Desirable skills

- Experience with Snowflake
- Distributed processing using Apache Spark through PySpark or Spark SQL
- Reporting, monitoring and analytical application tools: Tableau, Grafana, Streamlit or similar
- Snowflake semantic views or comparable semantic-layer technologies for self-service and AI-assisted analytics
- Experience preparing data for forecasting and predictive models

Tech Stack

Languages: SQL, Python
Data Warehouse & Database: Snowflake
Data Transformation & Processing: dbt, Apache Spark (PySpark and Spark SQL)
Container Tooling & Orchestration: Docker, Kubernetes
Data Applications, Visualization & Monitoring: Tableau, Streamlit, Grafana
Semantic Modelling: Snowflake semantic views
Version Control: Git
Engineering Practices: Code review, automated testing, CI/CD and documented production support procedures