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

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.

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.

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.

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.

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.

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.