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Senior Azure Data Engineer @ Cloudtech Consulting Sp. z o.o.

Senior Azure Data Engineer @ Cloudtech Consulting Sp. z o.o.

Cloudtech Consulting Sp. z o.o.Remote, Poland
1 dzień temu
Opis pracy

About The Role :

We are seeking a talented and experienced Azure Data Engineer to join our team for a long-term collaboration. You will play a key role in supporting and advancing data pipelines, which complements our mobile-first solution. You'll work with a modern and diverse tech stack in a fast-paced startup environment, contributing to innovative solutions that empowers advanced detection and recognition algorithms.

Responsibilities :

Maintain server and database Azure cloud infrastructure :

  • Azure Functions
  • Microsoft Fabric
  • Azure SQL
  • PostgreSQL
  • Design, optimize, and query Big Data databases / warehouses / lakes.

Develop, improve, and maintain data pipeline projects :

  • Python
  • FastAPI (Back-end API Development)
  • Computer Vision (OpenCV, PyTorch, etc.)
  • SQL
  • Build and maintain integrations with other parts of the ecosystem :

  • REST APIs
  • Continuous Integration / Deployment
  • Analytics dashboards
  • Automatic reporting and monitoring (e.g., Azure Monitor, Prometheus, Grafana)
  • Ensure our systems are secure, fast, and highly available.
  • Work with business requirements to provide insightful analytics, reports, and automations.
  • Nice to have :

  • Experience with visual data and image processing tools.
  • Machine learning models integration experience.
  • Hands-on experience in coding mid-size / large projects using Python.
  • Containerization (Docker, Kubernetes, Azure Kubernetes Service - AKS).
  • Infrastructure as Code (IaC) : e.g., Terraform, Bicep, ARM Templates.
  • Proven expertise :

  • 5+ years of experience in a hybrid Data Engineering, DevOps, or SRE role, with a
  • proven track record of designing and managing cloud-native data platforms.
  • Build, configured and deployed at least 1 data pipeline in Fabric that processed at least
  • 100GB of data,
  • Build and orchestrate end-to-end ETL / ELT workflows using Python, SQL, and Azure
  • services (Data Factory, Synapse, or Fabric).
  • Proven track record in query optimization, indexing, and partitioning for
  • high-performance data systems (PostgreSQL, Azure SQL, SQL Server).
  • Experience handling large datasets (Big Data, hundreds of millions of records) and  optimizing storage / performance trade-offs in cloud environments.
  • Integrating multiple data sources into Power BI, enabling self-service analytics and  interactive dashboards.
  • About Omnishelf :

    Omnishelf empowers small-format retailers (like convenience stores, liquor stores, and petrol stations) with real-time shelf intelligence through our advanced AI and Computer Vision platform. We enable store teams to use standard mobile devices to instantly scan shelves, detect out-of-stocks, improve planogram compliance, and ultimately boost sales.

    Our unique edge computing approach processes all data—scanning, analysis, reporting, and task creation—100% offline, directly on the device. This ensures fast, reliable store automation without any dependency on the cloud, eliminating downtime. We provide actionable insights to store employees and managers, driving efficiency and growth for retailers who are often underserved by complex technology. Our mission is : “Right Product. Right Shelf. Every Time.”

    This is remote position, but we will only consider candidates from Central / East Europe - prefferably Poland or Romania based

  • Good knowledge of Azure and Microsoft Fabric services and data pipelines.
  • Experience with SQL database engines, (Azure SQL, SQL Server, PostgreSQL).
  • Python language - ability to write required ETL transformations scripts, general
  • scripting and small codebase maintenance.
  • Knowledge in the area of BI / analytics / reporting / monitoring, e.g. Power BI (integration
  • between various data sources, visualization tools, processing pipelines).
  • The ability to work independently and collaboratively as part of a distributed team.
  • Business acumen - good communication skills, ability to transfer business
  • requirements into technical requirements and deliver them.
  • About The Role :

    We are seeking a talented and experienced Azure Data Engineer to join our team for a long-term collaboration. You will play a key role in supporting and advancing data pipelines, which complements our mobile-first solution. You'll work with a modern and diverse tech stack in a fast-paced startup environment, contributing to innovative solutions that empowers advanced detection and recognition algorithms.

    Responsibilities :

    Maintain server and database Azure cloud infrastructure :

  • Azure Functions
  • Microsoft Fabric
  • Azure SQL
  • PostgreSQL
  • Design, optimize, and query Big Data databases / warehouses / lakes.
  • Develop, improve, and maintain data pipeline projects :

  • Python
  • FastAPI (Back-end API Development)
  • Computer Vision (OpenCV, PyTorch, etc.)
  • SQL
  • Build and maintain integrations with other parts of the ecosystem :

  • REST APIs
  • Continuous Integration / Deployment
  • Analytics dashboards
  • Automatic reporting and monitoring (e.g., Azure Monitor, Prometheus, Grafana)
  • Ensure our systems are secure, fast, and highly available.
  • Work with business requirements to provide insightful analytics, reports, and automations.
  • Nice to have :

  • Experience with visual data and image processing tools.
  • Machine learning models integration experience.
  • Hands-on experience in coding mid-size / large projects using Python.
  • Containerization (Docker, Kubernetes, Azure Kubernetes Service - AKS).
  • Infrastructure as Code (IaC) : e.g., Terraform, Bicep, ARM Templates.
  • Proven expertise :

  • 5+ years of experience in a hybrid Data Engineering, DevOps, or SRE role, with a
  • proven track record of designing and managing cloud-native data platforms.
  • Build, configured and deployed at least 1 data pipeline in Fabric that processed at least
  • 100GB of data,
  • Build and orchestrate end-to-end ETL / ELT workflows using Python, SQL, and Azure
  • services (Data Factory, Synapse, or Fabric).
  • Proven track record in query optimization, indexing, and partitioning for
  • high-performance data systems (PostgreSQL, Azure SQL, SQL Server).
  • Experience handling large datasets (Big Data, hundreds of millions of records) and  optimizing storage / performance trade-offs in cloud environments.
  • Integrating multiple data sources into Power BI, enabling self-service analytics and  interactive dashboards.
  • About Omnishelf :

    Omnishelf empowers small-format retailers (like convenience stores, liquor stores, and petrol stations) with real-time shelf intelligence through our advanced AI and Computer Vision platform. We enable store teams to use standard mobile devices to instantly scan shelves, detect out-of-stocks, improve planogram compliance, and ultimately boost sales.

    Our unique edge computing approach processes all data—scanning, analysis, reporting, and task creation—100% offline, directly on the device. This ensures fast, reliable store automation without any dependency on the cloud, eliminating downtime. We provide actionable insights to store employees and managers, driving efficiency and growth for retailers who are often underserved by complex technology. Our mission is : “Right Product. Right Shelf. Every Time.”

    ,[Maintain server and database Azure cloud infrastructure, Design, optimize, and query Big Data databases / warehouses / lakes. , Develop, improve, and maintain data pipeline projects : , Build and maintain integrations with other parts of the ecosystem : , Ensure our systems are secure, fast, and highly available. , Work with business requirements to provide insightful analytics, reports, and automations.] Requirements : Azure, Microsoft Fabric, Azure Functions, Azure SQL, PostgreSQL, Python, OpenCV, FastApi, SQL, CI / CD Pipelines, REST API, Azure Monitor, Prometheus, Grafana, Kubernetes, Docker, Terraform, Bicep, AKS Tools : . Additionally : Small teams.

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