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Booking.com

Booking Holdings Romania - Data Analytics Engineer

Booking.com is hiring a Booking Holdings Romania - Data Analytics Engineer in Bucharest, Romania. Level rates it ; you can apply on Booking.com.

Level's read on this job

  • On Level's scale, this role is Little AI ●○○○: aI is not part of the work.
  • Level classifies it as a mid-level role.
  • Level lists 133 other open roles at Booking.com.

AI in this role

Booking Holdings Romania is a Center of Excellence based in Bucharest, Romania and was created to support the increasing business demands of the Booking Holdings Brands. The Center of Excellence provides access to specialized and highly skilled talent, leading industry best practices, and collaboration opportunities across all of our Brands.

As part of our Booking Holdings Romania team, you will have the opportunity to be a part of the world’s leading provider of online travel, with a mission of making it easier for everyone to experience the world through six-primary consumer facing brands: Booking.com, Priceline, Agoda, KAYAK, OpenTable and Rentalcars.com.



Role description

 

The Data Analytics Engineer operates as a fully independent contributor on data pipelines and data models of moderate complexity, supporting the cross-brand data, reporting, and analytics needs of Booking Holdings. This role takes end-to-end ownership of well-scoped deliverables—from ingestion through transformation to enablement of reporting—within an established framework set by senior peers and the Solutions Architect.

Success in this role means consistent, high-quality delivery on data products for the business areas served by the team (such as Finance, FP&A, Treasury, Procurement, Market Intelligence and others), while building a strong foundation in the team's tooling (Snowflake, dbt, Dagster, Python, AWS, Terraform) and growing technical judgment under guidance from Senior Engineers and the Solutions Architect.

 

This role provides a hybrid way of working with an onsite presence of 2 days/week.

 

 

Key Job Responsibilities and Duties

 

  • Pipeline Development: Builds and maintains data ingestion, transformation, and orchestration pipelines on Snowflake, dbt and Dagster (or Airflow/equivalent) under established team standards, ensuring data is delivered on time and to agreed quality.

  • Data Modeling on Snowflake: Implements dimensional data models on Snowflake based on requirements gathered by Business Analysts and Product, following team conventions for naming, layering, and documentation; exposure to other modeling approaches (e.g. Data Vault) is a plus.

  • Python for Data Analytics Engineering: Writes clean, testable Python—including object-oriented code—for Dagster assets, sensors and IO managers, as well as for general Data Analytics Engineering tasks.

  • Source Integration: Integrates data from a wide variety of sources (ERPs, transactional systems, SaaS tools, flat files, spreadsheets, etc.), with awareness of the relevant business semantics.

  • Quality, Testing & Monitoring: Writes tests (dbt tests, custom checks), implements basic data quality monitoring, and proactively investigates anomalies for the pipelines they own.

  • Operational Excellence (Awareness Level): Works within the team's CI/CD, Git, Docker, and Terraform setup with growing independence; contributes to infrastructure-as-code and DataOps practices under guidance of senior peers.

  • Collaboration: Partners daily with other Data Analytics Engineers, the Reporting team, Business Analysts and the Product Manager to translate requirements into pragmatic technical solutions, escalating ambiguity early.

  • Documentation & Knowledge Sharing: Documents the pipelines, models, and decisions they own and shares learnings with the team through code reviews and informal knowledge sessions.

 

Role Qualifications and Requirements

 

  • Bachelor’s Degree & 3-5 years of relevant experience

 

Knowledge (Success Foundations)

  • Solid understanding of relational and dimensional data modeling.

  • Strong understanding of cloud data warehousing, ideally on Snowflake.

  • Working knowledge of data warehousing methodologies (Kimball); Data Vault is a nice to have (*).

  • Awareness of business domains involving data and reporting (such as Finance, FP&A, Treasury, Procurement, Market Intelligence or comparable areas); transferable experience from other domains is also valued.

  • Awareness of data privacy and security concepts (GDPR/PII).

  • Nice to have (*): Awareness of Risk & Control / SOX concepts and how they apply to data and reporting.

 

Skills & Tools (Technical Ecosystem)

  • Strong SQL skills on Snowflake or a comparable cloud warehouse—including window functions, set operations, and performance basics.

  • Hands-on Snowflake experience: virtual warehouses, roles, basic performance tuning.

  • Hands-on experience with dbt (models, tests, macros, documentation).

  • Working knowledge of Python, including object-oriented Python — used for Dagster assets and Data Analytics Engineering tasks.

  • Familiarity with Dagster or Airflow (or another modern orchestrator).

  • Working knowledge of Git and CI/CD pipelines.

  • Working knowledge of AWS services for data workloads — S3 and at least one of Lambda or Glue — including basic IAM and CloudWatch awareness.

  • Terraform and infrastructure-as-code experience is a plus (*).

  • Docker and containerized workflows experience is a plus (*).

  • Awareness of BI / Dashboard concepts and semantic layers—sufficient to collaborate with the Reporting team.

  • Work experience with Streamlit (or similar lightweight data app frameworks) is a plus (*).

  • Hands-on experience using AI / LLM-based tools to accelerate engineering work—code generation, code review, debugging, documentation, and data exploration—is a plus (*).

 

Abilities (Behavioral Maturity)

  • Delivers well-scoped pipelines and models end-to-end, with quality, under the guidance of senior peers.

  • Breaks down requirements provided by Product/BAs into clear technical sub-tasks.

  • Communicates progress, risks, and blockers clearly and proactively.

  • Demonstrates curiosity about the broader data platform and the business domains served.

  • Excellent English communication skills (verbal and written).

 

Benefits & Perks

  • Contributing to a high-scale, complex, world renowned product and seeing real-time impact of your work on millions of travelers worldwide

  • Working in a fast-paced and performance driven culture

  • Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences and active community participation

  • Competitive compensation and benefits package 

  • Vast amounts of data to validate your ideas and the opportunity to experiment with real users

Booking Holdings is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. We strive to move well beyond traditional equal opportunity and work to create an environment that allows everyone to thrive.

 


Pre-Employment Screening

If your application is successful, your personal data may be used for a pre-employment screening check by a third party as permitted by applicable law. Depending on the vacancy and applicable law, a pre-employment screening may include employment history, education and other information (such as media information) that may be necessary for determining your qualifications and suitability for the position.

How we rate this

Booking Holdings Romania - Data Analytics Engineer at Booking.com rates 30 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

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