Senior Assistant Director (Data Engineer Lead), ETO

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 150,000 - 240,000; Full time

Job summary

A*STAR in Singapore seeks a Senior Data Engineer / Data Architecture Lead to shape and lead a Snowflake-based enterprise data platform. The role is hands-on, guiding teams through complex engineering decisions and leading lakehouse design, data modelling and AI-ready data product delivery.

You will own architecture patterns, engineering standards, data platform reliability and runbook automation while partnering with cybersecurity, governance, and AI teams to deliver scalable data solutions.

Qualifications

• Typically 10+ years of experience in data engineering, data warehouse or architecture roles.
• Proven hands-on experience designing and operating Snowflake or modern cloud data platform solutions at enterprise scale.
• Strong track record in data transformation, governance-by-design and platform optimisation.
• Experience leading engineers and owning architecture decisions.

Responsibilities

• Define target enterprise Snowflake architecture including account structure and environment strategy.
• Govern medallion architecture (Bronze/Silver/Gold/Platinum) for analytics and AI-ready consumption.
• Lead design of scalable ELT/ETL pipelines across enterprise source systems.
• Create reusable engineering frameworks for ingestion, transformation, validation, monitoring and deployment.
• Conduct architecture reviews, performance reviews and production readiness.
• Set engineering standards for SQL/Python/Snowflake objects and orchestration patterns.
• Drive platform observability, data freshness monitoring and security governance.

Skills

Snowflake architecture

Advanced SQL

Python

Lakehouse architecture

Data modelling

Team leadership

Azure Data Factory

CI/CD & DataOps

Education

Bachelor’s degree in CS/SE/IS/Data Eng

Master’s degree (advantageous)

Tools

Snowpark

Streamlit in Snowflake

Snowpipe

dbt

Airflow

Kafka

Job description

Role Summary

The Senior Data Engineer / Data Architecture Lead is a strategic and highly technical role responsible for shaping and leading the organisation’s Snowflake-based enterprise data platform. The role owns architecture patterns, engineering standards, data platform reliability, technical design reviews and team leadership for junior and mid-level data engineers. This person must be deeply hands-on, technically strong and capable of guiding teams through complex engineering decisions. The role is expected to lead Snowflake lakehouse design, data modelling, platform optimisation, DataOps, reusable engineering frameworks and AI-ready data product delivery.

Key Responsibilities

Enterprise Snowflake Architecture

• Define the target architecture for the enterprise Snowflake platform, including account structure, environment strategy, data domains, access patterns and data sharing design.
• Design and govern the Medallion Architecture across Bronze, Silver, Gold and Platinum layers for analytics, reporting and AI-ready consumption.
• Establish architectural blueprints, reference patterns, naming standards, modelling conventions and integration guardrails.
• Lead technical design for major data products, migrations, platform re-architecture and complex integrations.

Technical Engineering Leadership

• Lead the design and delivery of scalable, resilient and secure ELT/ETL pipelines across enterprise source systems.
• Create reusable engineering frameworks for ingestion, transformation, validation, monitoring, deployment and runbook automation.
• Conduct code reviews, architecture reviews, performance reviews and production readiness reviews.
• Set engineering standards for SQL, Python, Snowflake objects, orchestration patterns, testing and documentation.

Snowflake Platform Optimisation and Governance

• Optimise Snowflake warehouses, storage, clustering, data sharing and compute usage for performance and cost efficiency.
• Design RBAC, data access controls, masking policies and secure data sharing patterns in partnership with cybersecurity and governance stakeholders.
• Define platform observability, operational SLAs, data freshness monitoring and production support models.
• Drive adoption of Snowflake capabilities such as Snowpark, Streamlit in Snowflake, Cortex, Snowpipe, tasks/streams, Iceberg where suitable for organisational use cases.

People Management and Capability Building

• Manage, coach and mentor junior and mid-level Data Engineers, helping them grow into stronger technical contributors.
• Allocate engineering work, review delivery plans, remove technical blockers and ensure balanced workload across the team.
• Build a culture of engineering excellence, documentation discipline, knowledge transfer and reusable delivery patterns.
• Support recruitment, onboarding, technical assessments and competency development for the data engineering team.

Stakeholder Partnership and AI Readiness

• Partner with the CDO office, IT, enterprise architecture, cybersecurity, AI/Data Science teams and business functions to align engineering delivery to strategic outcomes.
• Translate business and AI use cases into scalable data platform and data product designs.
• Provide technical advisory to leadership on platform risks, investment needs, migration choices and future-state architecture.
• Ensure engineering deliverables include architecture documentation, lineage, runbooks and knowledge transfer to minimise vendor dependency.

Skills And Competancies

Core/ Madatory

• Expert Snowflake architecture
• Advanced SQL and performance tuning
• Python and data engineering design
• Lakehouse and data warehouse architecture
• Data modelling and semantic layer design
• Team leadership and technical mentoring
• Azure Data Factory / orchestration
• CI/CD and DataOps

Preferred/ Advantageous

• Snowpark
• Streamlit in Snowflake
• Snowflake Cortex
• dbt and Airflow
• Kafka or streaming patterns
• Iceberg / open table formats
• Alation or similar catalogues
• SAP / ERP integration experience

Experience And Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering or related discipline. Master’s degree is advantageous.
• Typically 10+ years of experience in data engineering, data warehouse, platform engineering or architecture roles.
• At least 5 years of experience leading engineers, mentoring technical teams or owning architecture decisions.
• Proven hands-on experience designing and operating Snowflake or modern cloud data platform solutions at enterprise scale.
• Strong track record in enterprise data transformation, migration, lakehouse architecture, governance-by-design and platform optimisation.