Data Engineer / Analytics Engineer (Mid-Senior)

emagine Polska; Lisboa; Presencial; EUR 65 000 - 85 000; Tempo integral

Resumo da oferta

emagine Polska is seeking a data engineer to bridge business needs with robust data infrastructure on Google Cloud. You will transform complex requirements into scalable data structures, focusing on semantic modeling and analytics readiness for AI agents.

Responsibilities include ETL/ELT design, BigQuery development, dimensional modeling, and collaboration with stakeholders to translate questions into data models, with emphasis on quality, documentation, and training handovers.

Qualificações

• A strong command of SQL with complex queries and performance tuning.
• Experience translating business questions into data models.
• Knowledge of semantic modeling and BI-ready data structures.
• Experience in retail / e-commerce data is a plus.

Responsabilidades

• Design and implement efficient ETL/ELT processes.
• Develop and optimize BigQuery solutions and data warehouses.
• Model data using Star/Snowflake schemas.
• Collaborate with stakeholders to capture requirements.
• Ensure data quality and documentation.
• Document developments for training teams.

Conhecimentos

Advanced SQL

Python

Dimensional modeling

Stakeholder communication

Requirements translation

Ferramentas

dbt

BigQuery

Looker/LookML

Airflow

Git

Descrição da oferta de emprego

A critical role connecting business needs and technical data infrastructure. Responsible for transforming complex business requirements into robust, scalable data structures ready for analysis within the Google Cloud Platform ecosystem. This position emphasizes semantic modeling, ensuring data is prepared for autonomous business consumption and valuable for decision-making, while also adapting it for advanced uses such as AI agents.

Main Responsibilities

• Data Preparation and Transformation: Design and implement efficient ETL/ELT processes.
• BigQuery Development: Optimize queries, manage partitions, and apply business logic in the Data Warehouse.
• Data Modeling: Apply dimensional modeling techniques (Star Schema/Snowflake).
• Requirements Translation: Engage directly with stakeholders to capture needs and translate business questions into data models.
• Quality and Validation: Ensure data consistency and integrity through rigorous testing and documentation.
• Documentation and Knowledge Transfer: Communicate and document technical developments for training teams to facilitate the diffusion of created solutions.

Key Requirements

• Advanced SQL proficiency
• Experience with dbt (data build tool)
• Experience in GCP (BigQuery)
• Knowledge of Python
• Proficiency in Looker / LookerML
• Experience with orchestration tools (e.g., Airflow)
• Dimensional data modeling
• Experience in Retail / E-commerce
• Version control (Git)
• Google Cloud certifications

Nice to Have

• Semantic Layer (LookerML): Development and maintenance of models in LookerML, defining dimensions and measures for self-service BI.