Senior Data Engineer

sciente international pte. ltd.; Singapore; Presencial; SGD 140.000 - 210.000; Jornada completa

Descripción de la vacante

SCIENTE International Pte. Ltd. is seeking a senior data engineer to design, build, and maintain production-grade data pipelines and lakehouse solutions in an on-premises environment.

You will drive data platform initiatives, performance, security, and technical best practices. The role requires 8+ years IT experience with 5+ years in data engineering, expert SQL/SQL Server, advanced Python & PySpark, and hands-on experience with Kubernetes/Docker, MongoDB, Cassandra, Iceberg, and Airflow.

Formación

• 8+ years IT experience with 5+ years in data engineering/data pipelines.
• Expert SQL/SQL Server – query optimization, indexing & performance tuning.
• Advanced Python & PySpark.
• Apache Spark and distributed data processing.
• Kubernetes & Docker for on-premises deployments.
• Data modelling – relational & NoSQL; hands-on MongoDB/Cassandra.
• Lakehouse/Data Lake architecture, including Iceberg, partitioning & metadata.
• ETL/ELT orchestration – Apache Airflow or similar.
• CI/CD, Git, automated testing & IaC.
• Data security, governance, RBAC, masking & compliance.

Responsabilidades

• Design and develop production-grade ETL/ELT pipelines using Python/PySpark.
• Build and manage multi-layer lakehouse architecture across raw, curated, and consumption layers.
• Deploy scalable data pipelines using Kubernetes/Docker in on-premise environments.
• Develop data models and optimize complex SQL Server queries and performance.
• Establish CI/CD, automated testing, monitoring, and version-control practices.
• Implement data security, governance, and access controls; mentor engineers, conduct code reviews, and drive engineering best practices.
• Collaborate with data scientists, BAs, and stakeholders to deliver business-ready datasets.
• Provide L3 support and lead resolution of complex data engineering challenges.

Conocimientos

8+ years IT experience

SQL/SQL Server

Python

PySpark

Apache Spark

Kubernetes

Docker

Data modelling (Relational & NoSQL)

MongoDB

Cassandra

Lakehouse/Iceberg

ETL/ELT orchestration (Airflow)

CI/CD & Git & IaC

Data security & governance

Herramientas

Apache Airflow

Iceberg

Kubernetes

Docker

Git

Terraform

Spark

MongoDB

Cassandra

Descripción del empleo

Job Summary

We are seeking a senior data engineer to design, build, and maintain production-grade data pipelines and lakehouse solutions in an on-premises environment, while driving data platform initiatives, performance, security, and technical best practices.

Mandatory Skill Set

• 8+ years ofIT experience with 5+ years in data engineering/data pipelines;
• Expert SQL/SQL Server – query optimization, indexing & performance tuning;
• Advanced Python & PySpark;
• Apache Spark and distributed data processing;
• Kubernetes & Docker for on-premises deployments;
• Data modelling – relational & NoSQL; hands-on MongoDB/Cassandra;
• Lakehouse/Data Lake architecture, including Iceberg, partitioning & metadata;
• ETL/ELT orchestration – Apache Airflow or similar;
• CI/CD, Git, automated testing & IaC;
• Data security, governance, RBAC, masking & compliance.

Desired Skill Set

• Experience in data virtualization, logical data warehouses, Data Mesh/Fabric architectures, metadata management, and data lineage;
• Familiarity with real-time streaming technologies such as Kafka/Flink, along with experience in technical leadership, mentoring, and Agile data architecture.

Responsibilities

• Design and develop production-grade ETL/ELT pipelines using Python/PySpark;
• Build and manage multi-layer lakehouse architecture across raw, curated, and consumption layers;
• Deploy scalable data pipelines using Kubernetes/Docker in on-premise environments;
• Develop data models and optimize complex SQL Server queries and performance;
• Establish CI/CD, automated testing, monitoring, and version-control practices;
• Implement data security, governance, and access controls;Mentor engineers, conduct code reviews, and drive engineering best practices;
• Collaborate with data scientists, BAs, and stakeholders to deliver business-ready datasets;
• Provide L3 support and lead resolution of complex data engineering challenges.

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