Description
Start date: 1st January 2025
End date (max up to): 31st December 2025
Location: Remote from Poland, candidates must be located in Poland.
Assignment context:
The client wants to scale out the usage of logging by normalizing raw logging to a normalized schema. The normalization is done using Microsoft tooling as part of the Azure landscape using a manual process. The normalization configuration needs to be placed within the Azure Monitor data pipeline and requires the identification of fields and values, next to the transformation to be done using KQL and Regex concepts. These need to be generated based on a small sample of logging and applied directly within the data pipeline.
Deliverables
At least 60% of the security log schema’s identified for 150 application are normalized by EOY
Testing the security log normalization
Applying of the configuration in the pipeline
Validation of the security log normalization with the relevant stakeholders
Ensuring data quality over-time including preventing the dropping of security logs
Knowledge skills:
Data savviness. You know your way with data and getting all the insights out. Keen on finding the ‘real’ problem that needs to be solved. Relentless but friendly, following processes;
Data engineering. Implementing data analytics from source to consumption. Knowledge of Azure-related pipelines is preferred.
Understanding of security logging in diverse formats and schema’s
Data Wrangling. KQL, Regex, Grok
Experience with SIEM solutions and detection engineering (3 years or more)
Banking or financial sector experience would be preferred
Please send in your CV if this role is of interest to you and if you match the above role
End date (max up to): 31st December 2025
Location: Remote from Poland, candidates must be located in Poland.
Assignment context:
The client wants to scale out the usage of logging by normalizing raw logging to a normalized schema. The normalization is done using Microsoft tooling as part of the Azure landscape using a manual process. The normalization configuration needs to be placed within the Azure Monitor data pipeline and requires the identification of fields and values, next to the transformation to be done using KQL and Regex concepts. These need to be generated based on a small sample of logging and applied directly within the data pipeline.
Deliverables
At least 60% of the security log schema’s identified for 150 application are normalized by EOY
Testing the security log normalization
Applying of the configuration in the pipeline
Validation of the security log normalization with the relevant stakeholders
Ensuring data quality over-time including preventing the dropping of security logs
Knowledge skills:
Data savviness. You know your way with data and getting all the insights out. Keen on finding the ‘real’ problem that needs to be solved. Relentless but friendly, following processes;
Data engineering. Implementing data analytics from source to consumption. Knowledge of Azure-related pipelines is preferred.
Understanding of security logging in diverse formats and schema’s
Data Wrangling. KQL, Regex, Grok
Experience with SIEM solutions and detection engineering (3 years or more)
Banking or financial sector experience would be preferred
Please send in your CV if this role is of interest to you and if you match the above role