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Rate 5 600 € - 6 600 € / month

Form of cooperation Full-time / 60% Remote

Sector Insurance

Location Bratislava

The reward is calculated upon delivery of 20 MD per month (1MD=8h)

Project duration 4-12 months with the possibility of extension

Period of cooperation 01.09.2026 - 31.12.2026

Start date 01.09.2026 ASAP or by agreement

Project duration 4-12 months with the possibility of extension

Period of cooperation 01.09.2026 - 31.12.2026

Start date 01.09.2026 ASAP or by agreement

Technology

SQL
Apache Spark
Azure Databricks

Languages

Project description

the project is focused on accelerating and improving the deployment of data science and machine learning solutions into production
- this is not a classic Data Engineering role focused only on ingestion, ETL pipelines, or data warehouse building
collaboration at the interface between:
- Data Science
- Data Engineering
- backend development
- cloud platform
- MLOps and production operations
the task will be to take over Python prototypes and notebooks from Data Scientists and transform them into robust production workflows in Azure Databricks
- the resulting solutions must be scalable, monitored, secure, maintainable, and deployable in a repeatable manner
collaboration in a small team, typically consisting of two to three Data Scientists and one Production/Data Engineer
tasks and responsibilities:
- productionalizing ML solutions
  - taking over prototypes created in Python or notebooks
  - refining experimental code to production quality
  - packaging and deploying models into production workflows
  - eliminating manual steps and reducing dependence on handoffs between teams
  - ensuring reliable operation of production models
- Data and ML pipelines
  - designing and implementing robust data and ML pipelines
  - automating data processing and model execution
  - configuring Databricks jobs and workflows
  - resolving dependencies between individual pipeline steps
  - implementing validation and control mechanisms
- MLOps and operations
  - versioning of code, data, models, and configurations
  - deployment automation
  - monitoring of production workflows and models
  - support for release processes
  - resolving operational issues and optimizing stability
- Databricks platform
  - co-management of the Azure Databricks environment
  - configuring jobs, workflows, and compute resources
  - optimizing performance and platform utilization
  - working with Apache Spark and Delta Lake
  - implementing standards for development and production deployment
- Governance and security
  - implementing data security policies
  - managing access permissions
  - adhering to governance standards
  - ensuring auditability and traceability of changes
  - supporting solutions in a regulated insurance environment
technologies used
- Must-have stack
  - Python
  - SQL
  - Azure Databricks
  - Apache Spark
  - Delta Lake
  - Data / ML Pipelines
  - Git
  - CI/CD
- Project stack
  - Databricks Jobs
  - Databricks Workflows
  - Python notebooks
  - Data Modelling
  - Data Quality
  - Model Deployment
  - Monitoring
  - MLOps
  - Access Management
  - Data Governance
- Nice-to-have
  - Azure Data Factory
  - Azure Storage
  - Azure Data Lake
  - MLflow
  - REST API
  - real-time data processing
  - Docker
  - Kubernetes
  - Azure DevOps
collaboration possible in a hybrid REMOTE + ON-SITE mode [2-3 days per week, Bratislava], or based on further agreement with the client
final remuneration depends on the candidate's experience and the selection process

Project requirements

min. 2 to 3+ years of experience in Data Engineering, backend development, or production development of data solutions
advanced practical knowledge of:
- Python
- SQL
experience with:
- Azure Databricks
- Apache Spark
- Delta Lake
experience with designing and implementing production data pipelines
demonstrable experience deploying solutions into a production environment
understanding of:
- data modeling
- performance optimization
- data quality
basic familiarity with machine learning workflows
ability to refine prototype code into production quality
active knowledge of the English language at a professional level (min. B2-C1)
a big advantage:
- experience with MLOps principles and tools
- MLflow or a similar model lifecycle management tool
- experience with the Azure ecosystem:
  - Azure Data Factory
  - Azure Storage
  - Azure Data Lake
- experience with APIs or real-time solutions
- automation of deployments and CI/CD
- monitoring of models and data pipelines
- experience from:
  - insurance industry
  - banking
  - other regulated enterprise environments
strong analytical and organizational skills
ability to solve problems effectively
independence and willingness to learn
strong interpersonal and communication skills (written and verbal)
team player

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