Vision.bi Ltd.
Overview
We are passionate data engineers who change the way organizations treat data. Since 2008, we’ve delivered high-standard, large-scale analytics projects for 200+ customers — from startups to global enterprises. Now, we’re looking for a technical Data Engineer to join our team and help us drive this vision forward.
As part of our professional services team, you’ll gain rapid, hands-on experience with multiple industries, business models, and high-volume datasets from day one. You’ll work with a modern tech stack — Snowflake, dbt, Rivery, and leading integration tools — to design and deliver data platforms that power analytics and AI.
What you’ll do
Build and optimize cloud-native data pipelines that handle large-scale, high-volume, and complex datasets across multiple industries.
Transform, clean, and model data using Snowflake + dbt, ensuring it is AI-ready, scalable, and business-friendly.
Integrate diverse sources (APIs, SaaS platforms, databases, streaming data) using Rivery and other best-in-class tools.
Collaborate with clients, analysts, and data scientists to design solutions that deliver trusted insights, automation, and new data products.
Implement best practices in performance tuning, security, governance, and monitoring to ensure reliability and compliance.
Drive innovation by bringing fresh ideas and adapting quickly to new business models and data challenges.
Gain hands-on experience quickly by working on multiple projects across different domains, customers, and industries.
What we’re looking for
B.A/B.Sc degree in Industrial Engineering, Information Systems or an equivalent background.
One to two years of hands-on experience, working as a data engineer or BI developer (developing data pipelines and ETL processes).
Excellent SQL and data modeling skills.
Experience with cloud data platforms (e.g., Snowflake).
Familiarity with data orchestration tools like Rivery.
Scripting/programming (Python preferred) is a plus.
Ability to handle high-volume, complex data environments.
Curiosity, adaptability, and passion for AI-driven data innovation.
Proactive, self-learner with strong problem-solving skills and the ability to independently tackle challenges.
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As part of our professional services team, you’ll gain rapid, hands-on experience with multiple industries, business models, and high-volume datasets from day one. You’ll work with a modern tech stack — Snowflake, dbt, Rivery, and leading integration tools — to design and deliver data platforms that power analytics and AI.
What you’ll do
Build and optimize cloud-native data pipelines that handle large-scale, high-volume, and complex datasets across multiple industries.
Transform, clean, and model data using Snowflake + dbt, ensuring it is AI-ready, scalable, and business-friendly.
Integrate diverse sources (APIs, SaaS platforms, databases, streaming data) using Rivery and other best-in-class tools.
Collaborate with clients, analysts, and data scientists to design solutions that deliver trusted insights, automation, and new data products.
Implement best practices in performance tuning, security, governance, and monitoring to ensure reliability and compliance.
Drive innovation by bringing fresh ideas and adapting quickly to new business models and data challenges.
Gain hands-on experience quickly by working on multiple projects across different domains, customers, and industries.
What we’re looking for
B.A/B.Sc degree in Industrial Engineering, Information Systems or an equivalent background.
One to two years of hands-on experience, working as a data engineer or BI developer (developing data pipelines and ETL processes).
Excellent SQL and data modeling skills.
Experience with cloud data platforms (e.g., Snowflake).
Familiarity with data orchestration tools like Rivery.
Scripting/programming (Python preferred) is a plus.
Ability to handle high-volume, complex data environments.
Curiosity, adaptability, and passion for AI-driven data innovation.
Proactive, self-learner with strong problem-solving skills and the ability to independently tackle challenges.
#J-18808-Ljbffr