In Innowise, you can not only develop as an expert in your field, solve complex problems and influence the result, but also see how the finished project affects the world around. We are a close-knit team of professionals who have already implemented 1100+ cases for clients from the USA, Denmark, Germany, etc. We need someone, who will strengthen our team and become part of the community!

To work successfully on projects you need:

3+ years of hands-on experience creating and maintaining data architectures, pipelines, and models
Proven experience of working with Azure data services—e.g., Azure Synapse, Databricks, Data Factory, Azure SQL, Power BI—to deliver scalable, reliable solutions
Proficiency with CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automating deployments and infrastructure changes
Familiarity with Agile methodologies (Scrum/Kanban) and ability to maintain clear documentation of processes, sprints, and user stories
Basic understanding or exposure to Data Science techniques, including Generative AI concepts, ensuring data readiness for ML/AI workloads
Strong communication skills to gather requirements from both technical and non-technical stakeholders and translate them into actionable data solutions
Understanding of data governance principles (e.g., privacy, compliance) and security best practices on Azure
Ability to monitor and optimize performance of ETL/ELT jobs, storage layers, and analytics workloads
At least B1+ level of English

Will be a plus:

Experience building or maintaining enterprise-grade data platforms, including cross-cloud integrations
Microsoft Certifications (e.g., Azure Data Engineer Associate) or other relevant credentials
Experience with Terraform, Bicep, or ARM for infrastructure-as-code
Familiarity with containerization and orchestration (Docker, Kubernetes)
Knowledge of metadata management and data cataloging tools (e.g., Purview, Collibra)

Main responsibilities:

Develop and optimize data pipelines on Azure (e.g., Databricks, Synapse, Data Factory) for ingestion, transformation, and consumption
Create reliable data models and structures, enabling various analytics and AI use cases
Automate deployment processes and infrastructure provisioning, ensuring smooth delivery from development to production
Continuously enhance CI/CD frameworks, applying best practices for code quality and performance
Enforce security and governance policies to comply with internal guidelines and external regulations
Collaborate closely with data scientists, analysts, and business teams to define requirements and deliver robust solutions