Data Engineer

Epergne Solutions; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Epergne Solutions in Singapore seeks a Data Engineer to join the team and accelerate the migration of legacy data pipelines to Databricks Delta Lake. You will help implement robust operational frameworks and ensure seamless integration with healthcare information systems and clinical data workflows.

The role involves leading critical projects, mentoring junior engineers, and collaborating with infrastructure and compliance teams to optimize real-time analytics and ML operations on the Databricks

Qualifications

• Minimum 5 years in system operations, compliance and management roles.
• Hands-on AWS platform experience is required.
• Experience migrating ETL pipelines to Databricks Delta Lake is preferred.

Responsibilities

• Monitor and maintain production data pipelines with 99.9% uptime.
• Implement logging, alerting, and monitoring systems.
• Lead migration of legacy ETL to Databricks Delta Lake architecture.
• Mentor junior team members and share knowledge.

Skills

Databricks

Spark

Monitoring

Cloud cost control

ML Ops

Education

Bachelor's degree in Computer Science or Computer Engineering

Tools

AWS

SageMaker

Oracle

Databricks tooling

Job description

This Data Engineer role is designed to augment our existing data engineering team to accelerate the implementation and operationalization of our enterprise data platform built on Databricks. The successful candidate will be responsible for leading the migration of legacy data pipelines to Databricks, establishing operational excellence frameworks, and ensuring seamless integration with existing healthcare information systems and clinical data workflows.

The role involves working on critical projects including the Healthcare Data Analytics Platform modernization and advanced analytics infrastructure supporting machine learning operations. The position holder will be expected to mentor junior team members.

Key projects include migrating existing ETL processes from traditional data warehouse systems to Databricks Delta Lake architecture, implementing real-time streaming analytics for clinical monitoring systems. The role requires close collaboration with infrastructure teams, compliance officers.

Role and Responsibilities

Operational Responsibilities:

• Monitor and maintain production data pipelines to ensure 99.9% uptime and optimal performance
• Implement comprehensive logging, alerting, and monitoring systems using Application monitoring tools
• Perform regular health checks performance, job execution times, and resource utilization to identify and resolve bottlenecks proactively
• Manage incident response procedures for pipeline failures, including root cause analysis, resolution, and post-incident reviews
• Establish and maintain disaster recovery procedures and backup strategies for critical data assets within the Databricks environment
• Conduct regular performance tuning of Spark jobs and Databricks cluster configurations to optimize cost and execution efficiency
• Maintain comprehensive documentation for operational procedures, runbooks, and troubleshooting guides
• Coordinate scheduled maintenance windows and system upgrades with minimal business impact
• Manage user access controls, workspace configurations, and security policies within Application environments

Requirements / Qualifications

Education & Experience:

• Degree in Computer Science or Computer Engineering
• Minimum 5 years working experience in system operations compliance and management areas
• Project hands-on experience specifically with AWS platform (primary requirement)
• project experience in cloud operations or cloud architecture
• Must be cloud certified (AWS)

Core Technical Skills:

• proficiency in Databricks platform, including workspace management, cluster configuration, and job orchestration
• Strong expertise in Apache Spark within Databricks environment, including Spark SQL, DataFrames, and RDDs
• Good in-depth understanding of data warehouse concepts, data profiling, data verification and advanced analytics techniques
• Strong knowledge of monitoring, incident management, and cloud cost control
• Databricks
• AWS cloud services and architecture
• Oracle Database management
• ML Ops practices within Databricks environment
• STATA for statistical analysis is advantage
• Amazon SageMaker integration with Databricks