Azure Databricks & Data Platform Ops Engineer

Lenovo

Kuala Lumpur

On-site

MYR 67,000 - 134,000

Full time

7 days ago

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Job summary

Lenovo is seeking a highly motivated Business Intelligence (BI) Engineer with strong expertise in Microsoft Azure, Databricks, and Data Platform Operations to join our Data & Analytics team. You will maintain and optimize modern cloud-based data platforms, supporting data pipelines, ensuring platform reliability, and enabling business intelligence and analytics solutions.

You will work closely with Data Engineers, BI Developers, Analytics teams, and business stakeholders to deliver scalable,

Qualifications

1 to 3 years of professional experience in Cloud Operations, Data Engineering, BI Engineering, or Data Platform Support.
Strong hands-on experience with Microsoft Azure services.
Minimum 1 year of experience with Azure Databricks, including: Cluster Management, Job Orchestration , Data Pipeline Development and Performance Optimization
Good understanding of: Data Warehouses, Data Lakes, ETL/ELT Processes andData Integration Concepts
Experience with monitoring and observability tools such as: Azure Monitor, Log Analytics, andDatadog
Strong SQL skills and experience working with large datasets.
Experience with Python and/or PySpark.
Knowledge of data governance, data quality, and security best practices.
Strong troubleshooting, problem-solving, and incident management skills.
Excellent communication and stakeholder management skills.

Responsibilities

Manage, monitor, and optimize Azure-based data platforms and Databricks environments.
Develop, maintain, and troubleshoot ETL/ELT data pipelines that support reporting and analytics needs.
Configure and administer Databricks clusters, jobs, workflows, and performance tuning activities.
Ensure data platform availability, reliability, and scalability through proactive monitoring and automation.
Implement monitoring, alerting, and logging solutions using Azure Monitor, Log Analytics, Datadog, and other observability tools.
Collaborate with BI and Data Engineering teams to design and support data models, data warehouses, and data lakes.
Analyze and resolve production incidents, data quality issues, and platform performance bottlenecks.
Support business intelligence initiatives by enabling trusted, high-quality data for reporting and analytics.
Drive cloud cost optimization and performance improvement initiatives across data platforms.
Participate in root cause analysis, documentation, and continuous improvement activities.
Contribute to DataOps and AIOps practices through automation, predictive monitoring, and operational excellence.
Ensure compliance with data governance, security, and organizational standards.