The evolving role of IT data with ML/AI integration

Leveraging data for anomaly detection and operational improvements

Shift from external SaaS platforms to internal solutions

Assembling a skilled technical team

Designing an optimal Core Data Lake (CDL) platform

Analyzing existing corporate products for IT data platform

Documenting architecture and obtaining approvals

Provisioning cloud services (Databricks, Azure Data Factory, etc.)

Implementing security measures and CI/CD pipeline

Introducing static code analysis tools

Integrating observability into Ops processes

Adopted the Scrum methodology to facilitate work tracking and planning.

Created two teams: one focused on platform delivery and the other on data team activities.

Delivered a fully integrated data ingestion and transformation platform using Azure Data Factory (ADF) and Databricks , fully integrated with corporate APIs and tools.

Implemented 1000+ data transformations in collaboration with data architects for accurate alignment with business needs.

Built a highly skilled technical team of 10 Data Engineers .

Successfully onboarded IT data into the company, reducing reliance on external platforms.

Developed and deployed an internal CDL platform for IT data, which facilitated the decommissioning of the SaaS solution .

Provided full support and integration with internal ML/AI platforms, enabling the next generation of observability , including automatic anomaly detection and AI-driven insights.

The project allowed for decommission of external SaaS platforms