Solution Architect for Data Modelling in Analytics
Hitachi Energy Services Sp. z o.o.
⚲ Kraków, Stare Miasto
Do uzgodnienia
Opis stanowiska
Nasze wymagania:
5+ years of experience in data architecture / data modelling / analytics solutions, with strong focus on logical data modelling
Proven experience designing enterprise data models (conceptual, logical, and physical) in complex data environments
Hands-on experience with Databricks (Delta Lake) and understanding of lakehouse architecture principles
Experience with data modelling tools (e.g., Erwin DM/DI or similar) including metadata and lineage management, Experience working with Azure data services (e.g., ADLS, ADF, Fabric) is an advantage
Strong knowledge of data modelling techniques (3NF, dimensional modelling, Data Vault concepts)
Ability to translate business requirements into scalable and reusable data structures, experience in global / distributed environments with strong communication skills in English
O projekcie:
The Solution Architect for Data Modelling is responsible for defining and governing enterprise-level data models across an analytics platform, ensuring consistency, scalability, and alignment with business and technical requirements. This role involves designing logical data models, establishing standards and best practices, collaborating with stakeholders and data engineers, and overseeing implementation in tools like Databricks. The architect also supports data governance, validates deliverables, drives continuous improvement in modelling practices and tooling, and acts as a subject matter expert guiding data architecture and platform evolution.
Zakres obowiązków:
Act as Solution Architect for Data Modelling, defining and governing logical and conceptual data models across the Analytics Platform (EDP)
Design and maintain enterprise-level logical data models ensuring consistency, reuse, and scalability across domains
Lead data modelling activities in Databricks (Delta Lake) and support integration with modelling tools such as Erwin DM/DI
Define and enforce data modelling standards, naming conventions, and best practices across the platform
Collaborate closely with Data Engineers to ensure correct implementation of models in Databricks
Align modelling approach with enterprise data architecture, governance frameworks, and data domain ownership (Data Mesh / Data Products where applicable)
Review and validate data models delivered by external vendors and delivery teams to ensure quality and compliance
Drive continuous improvement of modelling practices, tooling (Databricks, Fabric), and automation capabilities
5+ years of experience in data architecture / data modelling / analytics solutions, with strong focus on logical data modelling
Proven experience designing enterprise data models (conceptual, logical, and physical) in complex data environments
Hands-on experience with Databricks (Delta Lake) and understanding of lakehouse architecture principles
Experience with data modelling tools (e.g., Erwin DM/DI or similar) including metadata and lineage management, Experience working with Azure data services (e.g., ADLS, ADF, Fabric) is an advantage
Strong knowledge of data modelling techniques (3NF, dimensional modelling, Data Vault concepts)
Ability to translate business requirements into scalable and reusable data structures, experience in global / distributed environments with strong communication skills in English
O projekcie:
The Solution Architect for Data Modelling is responsible for defining and governing enterprise-level data models across an analytics platform, ensuring consistency, scalability, and alignment with business and technical requirements. This role involves designing logical data models, establishing standards and best practices, collaborating with stakeholders and data engineers, and overseeing implementation in tools like Databricks. The architect also supports data governance, validates deliverables, drives continuous improvement in modelling practices and tooling, and acts as a subject matter expert guiding data architecture and platform evolution.
Zakres obowiązków:
Act as Solution Architect for Data Modelling, defining and governing logical and conceptual data models across the Analytics Platform (EDP)
Design and maintain enterprise-level logical data models ensuring consistency, reuse, and scalability across domains
Lead data modelling activities in Databricks (Delta Lake) and support integration with modelling tools such as Erwin DM/DI
Define and enforce data modelling standards, naming conventions, and best practices across the platform
Collaborate closely with Data Engineers to ensure correct implementation of models in Databricks
Align modelling approach with enterprise data architecture, governance frameworks, and data domain ownership (Data Mesh / Data Products where applicable)
Review and validate data models delivered by external vendors and delivery teams to ensure quality and compliance
Drive continuous improvement of modelling practices, tooling (Databricks, Fabric), and automation capabilities
🔍 Dekoder Ogłoszenia
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experience in data architecture / data modelling / analytics solutions, with strong focus on logical data modelling
Oczekuje się głębokiego zrozumienia modelowania logicznego danych, co może oznaczać, że fizyczne aspekty modelowania będą mniej priorytetowe lub będą realizowane przez innych.
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Proven experience designing enterprise data models (conceptual, logical, and physical) in complex data environments
Może to oznaczać pracę nad istniejącymi, skomplikowanymi i potencjalnie trudnymi do zmiany modelami danych, a nie tworzenie ich od podstaw w nowym środowisku.
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Hands-on experience with Databricks (Delta Lake) and understanding of lakehouse architecture principles
Chociaż wymieniono Databricks, nacisk na 'understanding' może sugerować, że faktyczne, głębokie 'hands-on' doświadczenie nie jest kluczowe, a raczej teoretyczna wiedza.
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Experience with data modelling tools (e.g., Erwin DM/DI or similar) including metadata and lineage management
Wymagane jest doświadczenie z konkretnymi narzędziami, co może oznaczać, że będziesz musiał pracować z zastanym, być może przestarzałym oprogramowaniem, a nie wybierać najnowsze rozwiązania.
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Ability to translate business requirements into scalable and reusable data structures, experience in global / distributed environments with strong communication skills in English
Praca w globalnym/rozproszonym środowisku z silnymi umiejętnościami komunikacyjnymi może oznaczać konieczność radzenia sobie z różnicami kulturowymi, strefami czasowymi i potencjalnie niejasnymi wymaganiami odległych zespołów.