Senior /Lead Data Engineer

Newbridge; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Newbridge seeks a hands-on Data Engineer to build governed, audit-ready pipelines turning fragmented client data into AI-ready assets. You’ll partner with AI Engineers and Data Scientists to ensure scalable data flows from ERP, clinical and operations systems into a governed lakehouse.

You’ll own transformation, modeling and semantics, plus data quality and lineage for audits, embedding governance by design across APAC.

Qualifications

• 5-14 years of hands-on production data engineering, end-to-end delivery experience, and client-facing stakeholder management.
• Expert Python & SQL, dbt, Snowflake / Databricks, Airflow / Dagster, Iceberg / Delta Lake, plus strong understanding of data governance, vector stores, and cloud (Azure / AWS / GCP).

Responsibilities

• Build and operate end-to-end ingestion pipelines from ERP, clinical, customer and operational systems (batch + streaming) into a governed lakehouse.
• Own transformation, modelling, and semantic layer to ensure consistent business logic across BI and AI.
• Prepare and serve AI-ready data - curated datasets, embeddings, and RAG corpora.
• Own data quality, master data / entity resolution, and end-to-end lineage for audit readiness.
• Embed governance, privacy & security by design (PDPA and APAC data residency / cross-border rules).
• Build reusable accelerators and set engineering standards for the practice.

Skills

Python

SQL

dbt

Snowflake

Databricks

Airflow

Dagster

Iceberg

Delta Lake

data governance

vector stores

cloud

Tools

dbt

Snowflake

Databricks

Airflow

Dagster

Iceberg

Delta Lake

Azure

AWS

GCP

Job description

You will build governed, audit-ready pipelines that turn fragmented client data into trusted, AI-ready assets. This is a central, load-bearing role working alongside AI Engineers and Data Scientists.

Key Responsibilities:

• Build and operate end-to-end ingestion pipelines from ERP, clinical, customer and operational systems (batch + streaming) into a governed lakehouse.
• Own transformation, modelling, and semantic layer to ensure consistent business logic across BI and AI.
• Prepare and serve AI-ready data - curated datasets, embeddings, and RAG corpora.
• Own data quality, master data / entity resolution, and end-to-end lineage for audit readiness.
• Embed governance, privacy & security by design (PDPA and APAC data residency / cross-border rules).
• Build reusable accelerators and set engineering standards for the practice.

Must Have:

5-14 years of hands-on production data engineering, end-to-end delivery experience, and client-facing stakeholder management.

Expert Python & SQL, dbt, Snowflake / Databricks, Airflow / Dagster, Iceberg / Delta Lake, plus strong understanding of data governance, vector stores, and cloud (Azure / AWS / GCP).