Data Engineer (all genders)
Role details
Job location
Tech stack
Job description
We are growing our Data Language Team within the Gropyus Tech department. The Language team is responsible for the semantic layer of our Gropyus Data Fabric as well as data modeling and transformation for our self-service analytics.
Our team interacts with experts from various domains such as Digital Building Planning and Automation, Product Operations, Sustainability, AI, IoT, construction engineers, building architects, logistics experts, software engineering; solving complex challenges pertaining to end-to-end process for planning, building, and operating a building.
As part of the Data Language organization, you will:
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Design data models to formalize concepts from various architecture and construction domains.
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Contribute to the logic to transform and enrich our centralized data for self-service analytics
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Support Data Science use cases including Machine Learning and AI
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Collaborate with domain experts and software engineers to understand data needs and deliver high-quality datasets.
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Implement and uphold data quality, governance, and security standards, including monitoring, testing, and documentation.
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Adhere to best practices and rigor in development including documentation, data governance, testing, and validation
Requirements
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Experience working with a tech stack similar to:
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Programming languages like Python or Kotlin
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Query Languages like: SPARQL, SQL
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Data Reporting like: PowerBI, Tableau, Quick Sight
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Databases like Postgres, BigQuery, Spark, Graph DB
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Cloud Storage Platforms
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Ability to complete work as directed with guidance from senior engineers or leadership
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Experience resolving issues related to data discrepancies and inconsistencies and creating validation and testing for prevention and handling
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You have data modeling experience through semantic or Business Intelligence development
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You have experience following best practice guidelines in data and software engineering
Optional Experience:
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Some knowledge about semantic layer or ontologies
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Experience with graph technologies and triples
- Data Science, Machine Learning and AI agents