Senior Data Engineer

Our client is one of Singapore’s largest and fastest-growing multi-specialty private healthcare groups, committed to transforming the healthcare landscape through collaboration, innovation, and patient-centric care.

They are setting up operations in Malaysia and they are looking for a Senior Data Engineer to join their tech team.

Key Responsibilities

1. Data Architecture & Pipeline Engineering

 Design, build, and own scalable, fault-tolerant ETL/ELT pipelines for structured and unstructured data.

 Architect batch and real-time (streaming/event-driven) data pipelines to support operational and analytical use cases.

 Develop robust ingestion frameworks across APIs, databases, and third-party systems.

 Own end-to-end data flow design, from ingestion to consumption, ensuring performance, scalability, and cost efficiency.

 Manage schema evolution and ensure backward compatibility across systems.

2. Data Platform & Modelling

 Design and manage data lake / warehouse / lakehouse architectures.

 Develop efficient data models (dimensional, normalized, and semantic layers) to support BI and analytics.

 Optimize storage, partitioning, and query performance at scale.

 Enable self-service analytics through well-structured, discoverable datasets.

 Support diverse access patterns across analytics, reporting, and product use cases.

3. Data Quality, Reliability & Observability

 Implement robust data quality frameworks (validation, reconciliation, anomaly detection).

 Define SLAs, monitoring, alerting, and incident response for data systems.

 Drive improvements in data observability (freshness, lineage, accuracy).

 Troubleshoot and resolve production data issues with a focus on root cause analysis.

4. Platform Engineering & DevOps

 Implement CI/CD pipelines for data workflows and infrastructure.

 Apply infrastructure-as-code and automation to manage scalable data systems.

 Ensure systems are secure, cost-efficient, and production-grade.

 Continuously improve system performance, reliability, and deployment processes.

5. Analytics & Business Enablement

 Partner with business stakeholders to translate requirements into scalable data solutions.

 Enable BI tools (e.g., Power BI, Tableau) with clean, reliable datasets and consistent metrics.

 Support advanced analytics, experimentation, and ad hoc data exploration.

 Establish consistent definitions, metrics, and semantic layers across the organization.

6. Collaboration, Leadership & Governance

 Work cross-functionally with engineering, product, analytics, and data science teams.

 Mentor junior data engineers and contribute to raising engineering standards.

 Promote best practices in data governance, privacy, and security.

 Document systems, pipelines, and data models to ensure maintainability and transparency.

7. Experimentation & Innovation

 Build sandbox environments and rapid experimentation pipelines for new data sources.

 Evaluate and onboard new tools, technologies, and data sets.

 (Optional) Support emerging use cases such as AI/ML or GenAI-enabled data workflows.

What We’re Looking For

Must-Have

 5+ years of experience in Data Engineering or related roles.

 Strong SQL and Python skills; proficiency in building production-grade pipelines.

 Experience with both batch and streaming data processing.

 Hands-on experience with data modelling, data warehouses, and ETL/ELT systems.

 Experience designing scalable, reliable data architectures.

 Strong understanding of data quality, monitoring, and observability.

 Proven ability to own systems end-to-end and work with ambiguous requirements.

Strong Plus

 Experience with distributed data processing frameworks (e.g., Spark, Flink).

 Experience with orchestration tools (e.g., Airflow, Prefect).

 Cloud experience (AWS, GCP, Azure) and modern data stack tools.

 Familiarity with CI/CD, DevOps practices, and infrastructure-as-code.

 Experience with BI tools (Power BI, Tableau).

 Understanding of data governance, lineage, and security practices.

Nice-to-Have

 Experience with streaming platforms (e.g., Kafka, Kinesis).

 Exposure to ML/AI or GenAI data workflows.

 Experience with data cataloging and metadata management tools.

 Domain experience (e.g., healthcare, finance, or similar regulated environments).

Success Indicators

 Scalable, reliable data systems with high availability and performance.

 Strong data quality and minimal production incidents.

 Fast, flexible data access enabling business and analytics teams.

 Well-architected, maintainable, and documented data platforms.

 Positive impact on team capability through mentorship and leadership.