Senior Data Engineer - Lakehouse & Data Pipelines

NTT SINGAPORE PTE. LTD.; Singapore; Sur place; SGD 120 000 - 180 000; Plein temps

Résumé du poste

NTT SINGAPORE PTE. LTD. seeks a Senior Data Engineer to design, build, productionise and operate trusted data pipelines on the SWDA data platform using AWS and Databricks. You will provide technical leadership across ingestion, transformation, modelling, quality, lineage, orchestration and performance.

The role emphasises CRISPr principles—comprehensive, robust and scalable data systems—to ensure data remains dependable for analytics, reporting and ML workloads across the organization.

Qualifications

• Degree in CS/SE/Data Engineering or related field.
• 6–10 years of data/software engineering experience with production pipelines.
• Experience leading data services architecture and standards.

Responsabilités

• Lead design and delivery of batch and streaming pipelines across AWS and Databricks.
• Design Bronze/Silver/Gold layers and reusable data products with ownership and definitions.
• Implement reliable ingestion from databases, files, APIs and streams.
• Define dashboards, monitoring and reliability metrics for pipelines.
• Collaborate with stakeholders to translate analytics needs into data specs.

Connaissances

Python

PySpark

SQL

Databricks

Apache Spark

Delta Lake

AWS data engineering

Lakehouse design

Change Data Capture

Data quality

Formation

Bachelor's degree in Computer Science or related field

Outils

Databricks

Apache Spark

Delta Lake

AWS stack (S3, Glue, Lake Formation, IAM, KMS)

Description du poste

NTT SINGAPORE PTE. LTD. seeks a Senior Data Engineer to design, build, productionise and operate trusted data pipelines on the SWDA data platform using AWS and Databricks. You will provide technical leadership across ingestion, transformation, modelling, quality, lineage, orchestration and performance.

The role emphasises CRISPr principles—comprehensive, robust and scalable data systems—to ensure data remains dependable for analytics, reporting and ML workloads across the organization.