Senior/ Lead Data Engineer
TypeFull-time job
LocationSingapore
Posted1 hour ago
Our client is a global consulting firm operating from 30+ offices worldwide across the Americas, Europe, and Asia-Pacific.
As a trusted advisor to the Office of the CFO, they partner with management teams and private equity sponsors to solve complex finance, accounting, and transformation challenges - free from audit independence constraints.
The Mandate:
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:
5-10 years of hands-on production data engineering, end-to-end delivery experience, and client-facing stakeholder management.
Tech Stack:
Expert Python & SQL, dbt, Snowflake / Databricks, Airflow / Dagster, Iceberg / Delta Lake, plus strong understanding of data governance, vector stores, and cloud (Azure / AWS / GCP).
Originally posted on Himalayas
As a trusted advisor to the Office of the CFO, they partner with management teams and private equity sponsors to solve complex finance, accounting, and transformation challenges - free from audit independence constraints.
The Mandate:
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:
5-10 years of hands-on production data engineering, end-to-end delivery experience, and client-facing stakeholder management.
Tech Stack:
Expert Python & SQL, dbt, Snowflake / Databricks, Airflow / Dagster, Iceberg / Delta Lake, plus strong understanding of data governance, vector stores, and cloud (Azure / AWS / GCP).
Originally posted on Himalayas
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