Senior Assistant Director (Data Engineer Lead), ETO

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.