Back
Tier-1 brand, mid-level generalist data role, metro location, and common skillset amplify competition.
Core data engineering skills are transferable, but KQL and healthcare IoT/log focus add moderate specificity.
Explicit 5–6 years requirement plus mandatory KQL/Azure/Databricks and log-processing skills make filtering strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and maintain scalable data pipelines for large-scale machine log data spanning 15–20 years.
Develop, optimize, and monitor log processing systems and analytics platforms on Azure to support predictive and proactive maintenance.
Ensure data quality, operational stability, and integration with downstream AI systems such as OneAI and MI Log Interpreter.
5–6 years of experience in data engineering with expertise in building data pipelines and ETL/ELT processes.
Proficient in Kusto Query Language (KQL) and Azure Data Explorer.
Experience with Azure services including Azure Functions, Event Grid, Data Factory, Databricks/Spark, and Delta Lake.
Strong programming skills in Python and C#; experience with log data processing and analysis.
Experienced in handling and optimizing large-scale historical machine log data and distributed data processing environments.
Demonstrated ability to monitor and optimize pipeline performance for operational reliability and efficiency.
Collaborates effectively in small cross-functional teams and possesses a data product ownership mindset (development plus operations).