Data consultant
⚲ Kraków, Poznan, Wrocław, Łódź, Warszawa
15 960 - 21 672 PLN (B2B)
Wymagania
- Data engineering
- SQL
- Python
- Data modelling
- Data warehouse
- Data Lake
- GCP
- dbt
- Airflow
- Trino
- CI/CD
- BigQuery
- Bi tools (nice to have)
- Power BI (nice to have)
- Qlik (nice to have)
- Apache Superset (nice to have)
- Snowflake (nice to have)
- Databricks (nice to have)
- Spark (nice to have)
- PySpark (nice to have)
Opis stanowiska
O projekcie:
What will you do?
As a Data Engineer, you will design and develop modern data platforms and data products in cloud and on-premise environments. You will work closely with business and technical teams to build scalable data solutions, onboard new data sources, and optimize data processing pipelines that support analytics and reporting across the organization.
Wymagania:
- 3+ years of commercial experience as a Data Engineer- Strong knowledge of SQL and Python- Experience with data modelling- Understanding of Data Warehousing, Data Lakes and Data Lakehouse concepts- Hands-on experience with GCP data engineering tools- Very good knowledge of BigQuery- Experience with DBT, Airflow and Trino- Understanding of CI/CD practices
Nice to have
- Experience with Spark/PySpark- Experience with BI tools such as Power BI, Qlik or Apache Superset- Experience with Snowflake- Experience with Databricks
Codzienne zadania:
- Design, build, test and deploy cloud and on-premise data models and transformations
- Develop ETL processes and data pipelines
- Optimize data views and structures for reporting and visualization use cases
- Review, refine and implement business and technical requirements
- Collaborate with teams on backlog refinement, user stories and epics in Jira
- Onboard new data sources and develop ingestion, warehousing and data products
What will you do?
As a Data Engineer, you will design and develop modern data platforms and data products in cloud and on-premise environments. You will work closely with business and technical teams to build scalable data solutions, onboard new data sources, and optimize data processing pipelines that support analytics and reporting across the organization.
Wymagania:
- 3+ years of commercial experience as a Data Engineer- Strong knowledge of SQL and Python- Experience with data modelling- Understanding of Data Warehousing, Data Lakes and Data Lakehouse concepts- Hands-on experience with GCP data engineering tools- Very good knowledge of BigQuery- Experience with DBT, Airflow and Trino- Understanding of CI/CD practices
Nice to have
- Experience with Spark/PySpark- Experience with BI tools such as Power BI, Qlik or Apache Superset- Experience with Snowflake- Experience with Databricks
Codzienne zadania:
- Design, build, test and deploy cloud and on-premise data models and transformations
- Develop ETL processes and data pipelines
- Optimize data views and structures for reporting and visualization use cases
- Review, refine and implement business and technical requirements
- Collaborate with teams on backlog refinement, user stories and epics in Jira
- Onboard new data sources and develop ingestion, warehousing and data products
🔍 Dekoder Ogłoszenia
🔴
design and develop modern data platforms and data products in cloud and on-premise environments
Może oznaczać zarówno budowanie od zera, jak i pracę z istniejącymi, potencjalnie przestarzałymi systemami.
🔴
work closely with business and technical teams
Może oznaczać zarówno konstruktywną współpracę, jak i konieczność ciągłego tłumaczenia technicznych zagadnień nietechnicznym osobom.
🔴
optimize data processing pipelines that support analytics and reporting across the organization
Może oznaczać optymalizację istniejących, problematycznych procesów, a nie budowanie nowych, wydajnych rozwiązań.
🔴
review, refine and implement business and technical requirements
Może oznaczać, że wymagania są niejasne lub często się zmieniają, wymagając ciągłego doprecyzowania i adaptacji.
🔴
onboard new data sources and develop ingestion, warehousing and data products
Może oznaczać pracę z bardzo różnorodnymi i często chaotycznymi źródłami danych, wymagającymi dużego nakładu pracy przy integracji.