Data Engineer / Analytics Engineer (Mid-Senior)
⚲ Lisbon
Do uzgodnienia
Wymagania
- Documentation
- Business Intelligence (BI)
- Data Warehouse (DW)
- training
- Operations
- Artificial Intelligence (AI)
- DataStage (ETL)
- SQL
- Data modeling
- ETL
Opis stanowiska
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.
2. 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.
3. 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
4. Nice to Have
• Semantic Layer (LookerML): Development and maintenance of models in LookerML, defining dimensions and measures for self-service BI.
2. 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.
3. 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
4. Nice to Have
• Semantic Layer (LookerML): Development and maintenance of models in LookerML, defining dimensions and measures for self-service BI.
🔍 Dekoder Ogłoszenia
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connecting business needs and technical data infrastructure
Będziesz musiał dużo rozmawiać z ludźmi z biznesu, którzy mogą nie mieć technicznej wiedzy o danych.
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autonomous business consumption
Oczekuje się, że dane będą tak dobrze przygotowane, że użytkownicy biznesowi będą mogli samodzielnie je analizować bez ciągłego wsparcia technicznego.
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adapting it for advanced uses such as AI agents
Może to oznaczać, że będziesz musiał pracować nad danymi, które będą używane do trenowania modeli AI, co może wymagać dodatkowych umiejętności lub wiedzy.
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Engage directly with stakeholders to capture needs
Będziesz odpowiedzialny za zbieranie wymagań od różnych osób, co może być czasochłonne i wymagać umiejętności negocjacyjnych.
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facilitate the diffusion of created solutions
Oprócz tworzenia rozwiązań, będziesz musiał aktywnie pomagać innym w ich zrozumieniu i wykorzystaniu.