Senior Manager (Data Engineer), iCORE

A*STAR Research Entities (A*STAR); Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

iCORE, a research entity within A*STAR, is seeking a Senior Data Engineer to own end-to-end data pipelines and manage a Snowflake data warehouse. You will enforce data quality, implement scalable transformations, and ensure secure access controls while coordinating with BMRC institutes and external vendors.

You will work closely with the Business Data Analyst to ensure clean, trusted data for dashboards (Power BI) and upcoming data hub initiatives across BMRC IIs.

Qualifications

• 5–8 years of hands-on data engineering experience, with at least 2 years in a senior role.
• Experience owning end-to-end data architecture for multi-source integrations.
• Strong communication with technical and non-technical stakeholders.
• Experience working in a research/institutional environment is advantageous.

Responsibilities

• Design and maintain data pipelines across iCORE data workstreams.
• Migrate transformation logic from Power BI to Snowflake using dbt or equivalents.
• Manage Snowflake cost and performance optimisation.
• Enforce data quality, observability, and RBAC across systems.
• Collaborate with analysts and external vendors on integration requirements.

Skills

SQL

Snowflake

ETL/ELT

dbt

Airflow

Cloud platforms

API integration

SAP ERP

Education

Bachelor's or Master's in CS/DE/IS

Tools

Power BI

Git

Job description

iCORE (Integrated Core Research Operations & Excellence) is a research entity within A*STAR, restructured from the Research Support Centre (RSC). iCORE primarily supports the Biomedical Research Council (BMRC) cluster, while the Strategic Initiatives team operates across the broader A*STAR organisation, driving digital transformation, data infrastructure, and process innovation to accelerate scientific discovery and institutional effectiveness.

Role Overview

We are looking for a Senior Data Engineer to join the Strategic Initiatives team at iCORE. This is a hands-on, end-to-end ownership role. On a typical day, the engineer will be designing and maintaining data pipelines, managing and optimising the Snowflake data warehouse, enforcing data quality across ingestion and transformation layers, and managing access controls — while also engaging with research institute counterparts, ITSS, and external vendors to align on integration requirements and data standards.

The role supports iCORE’s two active data workstreams:

• the iCORE dashboards, including Asset Lifecycle Management (ALCM) and platform performance metrics dashboard built on SAP ERP, Snowflake, and Power BI;
• and a forthcoming lab digitalisation initiative to build a centralised research data hub consolidating data from disparate laboratory systems (LIMs, ELN, integrated platform, etc..) across BMRC research institutes.

This role works in close partnership with the Business Data Analyst, who leads stakeholder requirement gathering, analytics, and reporting. The Data Engineer focuses on the infrastructure layer — ingestion, transformation, modelling, and pipeline reliability — ensuring the analyst has clean, consistent, and trusted data to work with.

Key Responsibilities

Data Pipeline Development & Integration

• Own and maintain data pipeline infrastructure across iCORE’s data workstreams, including pipelines supporting iCORE dashboards built on SAP ERP, Snowflake, and Power BI.
• Build and maintain API integrations and ETL/ELT pipelines to ingest data from heterogeneous source systems — including SAP ERP, OEM equipment software, and laboratory systems — in a reliable and scalable manner.
• Lead the migration of data transformation logic from Power BI (Power Query / DAX) into Snowflake, implementing scalable, version-controlled SQL transformations using best practices (e.g. dbt or equivalent).
• Maintain and improve pipeline orchestration, scheduling, and alerting to ensure data is accurate and up to date.
• Manage pipeline code using version control (Git), enforce code review practices, and where applicable implement automated testing and deployment of pipeline changes.
• Collaborate with the Business Data Analyst to validate transformed data, resolve discrepancies, and ensure the semantic layer meets reporting requirements.

Data Modelling & Warehouse Management

• Establish and enforce data modelling standards (e.g. layered architecture with raw, staged, and mart layers) to ensure the Snowflake warehouse remains maintainable and scalable over time.
• Own the design and ongoing optimisation of the Snowflake data warehouse, including table structures, clustering strategies, materialised views, and partitioning to ensure query performance and cost efficiency.
• Define and enforce data schemas, naming conventions, and field-level standards across integrated systems to ensure data consistency and interoperability.
• Monitor and manage Snowflake compute and storage costs, implementing best practices to optimise resource usage as data volumes and pipeline complexity grow.

Data Quality & Observability

• Implement automated data quality checks across ingestion and transformation layers to catch anomalies, schema drift, and data inconsistencies before they surface in downstream dashboards or reports.
• Establish pipeline monitoring, alerting, and logging practices to ensure failures or data quality issues are detected and resolved proactively.
• Define and track data quality metrics (e.g. completeness, accuracy, timeliness) for key data domains, working with the Business Data Analyst to validate outputs against business expectations.

Access Control & Data Security

• Manage role-based access control (RBAC) within Snowflake, ensuring data access is appropriately scoped by user role, team, and data sensitivity.
• Work with ITSS and compliance stakeholders to ensure data pipelines and storage practices align with A*STAR’s data governance policies and relevant data protection requirements.
• Apply appropriate controls for sensitive research and equipment data ingested from BMRC research institutes and OEM systems.

Cross-Functional Engagement

• Engage proactively with external vendors and ITSS to align on integration requirements, data formats, and API specifications.
• Coordinate with counterparts across BMRC research institutes to support requirement validation and integration outputs, working alongside the Business Data Analyst who leads stakeholder requirement gathering.
• Represent iCORE Strategic Initiatives in cross-RI and A*STAR-wide technical working groups related to data infrastructure and digitalisation.
• Maintain pipeline documentation, field mapping specifications, and data dictionaries at a level sufficient for team maintainability and onboarding — with the expectation that dedicated data governance resources may be introduced in future.

Additional Responsibilities

Beyond day-to-day engineering work, the following responsibilities contribute to the team’s longer-term strategic direction and will become increasingly prominent as iCORE’s data capabilities mature.

Data Hub Architecture & Platform Evaluation

• Design and own the end-to-end architecture of a centralised research data hub integrating data from LIMS, ELN, WMS, sample management systems, and other sources across BMRC research institutes.
• Evaluate and recommend appropriate platforms, tools, and architectural patterns for the data hub as requirements evolve.
• Lead proof-of-concept exercises to assess new technologies and integration approaches, providing evidence-based architecture recommendations to inform strategic decisions.

Governance & Documentation

• Develop and maintain data governance artefacts including data dictionaries, lineage diagrams, and integration specifications.
• Support the Senior Assistant Director with technical input for management reporting and strategic planning on data infrastructure.

Strategic Alignment

• Drive alignment on data integration standards and digitalisation approaches across BMRC research institutes, while maintaining active engagement with SERC counterparts to ensure compatibility with broader A*STAR-wide digitalisation efforts over time.

Skills and Qualifications

Must Have

• SQL advanced data modelling Snowflake
• ETL ELT pipeline development eg dbt Airflow or equivalent
• Cloud data platforms AWS Azure or GCP
• API integration REST SOAP
• SAP ERP data extraction and integration

Good to Have

• Python or scripting for data automation
• Power BI Power Query DAX for collaboration with the BDA
• Experience with lab scientific systems LIMS ELN WMS
• Familiarity with SAP data models beyond ERP
• Data governance and cataloguing tools
• Data transformation best practices and version control Git

In addition candidates should have:

• A Bachelors or Masters degree in Computer Science Data Engineering Information Systems or a related discipline
• 5 8 years of hands on data engineering experience with at least 2 years in a senior or lead capacity
• Demonstrated experience owning end to end data architecture for multi source integration projects
• Strong communication skills and the ability to engage effectively with both technical and non technical stakeholders
• Experience working in a research scientific or institutional environment is advantageous

What we Offer

• A meaningful role at the intersection of data engineering and scientific research, with direct impact on how A*STAR’s BMRC research institutes operate.
• Opportunity to shape data infrastructure at scale within a high-visibility team that spans the breadth of A*STAR.
• Collaborative team environment with strong leadership support and cross-RI exposure.
• Competitive A*STAR remuneration package commensurate with experience.