rms. The ideal candidate should have strong expertise in modern data engineering, cloud-based data platforms, API integrations, and enterprise data pipeline development.
The candidate will be responsible for designing, developing, and maintaining scalable data pipelines that integrate ServiceNow, observability platforms, runbooks, knowledge repositories, RCA documents, and other enterprise data sources into AI platforms. The role includes extracting and transforming operational data, building robust ingestion frameworks, creating optimized data models, managing vector databases, and preparing high-quality datasets for AI and machine learning applications.
Professionals should have hands-on experience with Python, SQL, Azure Data Factory, Azure Databricks, API integration, data modeling, and ServiceNow data integration. Strong knowledge of ETL/ELT processes, cloud data engineering, data quality, data lineage, metadata management, and enterprise data architecture is highly preferred.
The role also involves collaborating with AI engineers, data scientists, solution architects, and business stakeholders to deliver reliable, scalable, and secure data solutions. Candidates will optimize SQL queries, improve data processing performance, ensure data governance, and support AI-driven initiatives by enabling high-quality data pipelines. Strong analytical, communication, stakeholder management, and problem-solving skills are essential.
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