Booking Holdings Romania - Data Analytics Engineer
Indexed description
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.
- Bachelor’s Degree & 3-5 years of relevant experience
- 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.
- 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 (*).
- 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).
- 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
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.
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