Role Summary

The Data Engineer will design, build and maintain reliable data pipelines and reusable datasets on a Snowflake-centric data platform. The role supports the delivery of trusted data products for analytics, reporting, governance and AI use cases.

This position requires a hands-on engineer who can work with source systems, ingestion tools, Snowflake, transformation logic, data quality checks and documentation. The candidate should be comfortable collaborating with analysts, business users, governance teams and senior engineers to ensure data is accurate, secure, performant and easy to consume.

Responsibilities

Snowflake Engineering and Development

• Develop and maintain Snowflake databases, schemas, tables, views, tasks, streams and stored procedures as required.
• Build ELT/ETL pipelines that move data from source systems into Snowflake using approved integration patterns.
• Optimise SQL transformations and warehouse usage for performance, cost and maintainability.
• Support implementation of Medallion Architecture layers such as Bronze, Silver, Gold and Platinum where applicable.

Data Integration and Pipeline Delivery

• Build ingestion pipelines from enterprise systems such as ERP, HR, procurement, finance, research administration and flat-file sources.
• Support batch, incremental, CDC and near-real-time ingestion patterns based on business requirements.
• Develop reusable components for extraction, transformation, validation, error handling and monitoring.
• Troubleshoot data pipeline failures and work with source system owners to resolve integration issues.

Data Quality, Testing and Reliability

• Implement data validation, reconciliation, profiling and exception-handling checks across pipelines.
• Monitor pipeline reliability, data freshness, load performance and recurring operational issues.
• Document quality rules, known limitations and remediation steps for supported data products.
• Support root-cause analysis and corrective actions for data defects identified by analysts or business users.

Governance, Security and DataOps

• Implement role-based access controls, masking or security controls in line with platform standards.
• Support metadata, lineage and catalogue integration by documenting source-to-target mappings and transformation logic.
• Use Git and CI/CD practices to manage code, versioning and deployment across environments.
• Contribute to engineering standards, coding conventions, templates and runbooks.

Skills and Competencies:

Core / Mandatory

• Snowflake development and optimisation
• SQL and stored procedures
• Python for data engineering
• Azure Data Factory or similar orchestration
• Data modelling and ELT patterns
• Git and CI/CD basics

Preferred / Advantageous

• dbt
• Airflow
• Snowpipe and streams/tasks
• Snowpark
• Data quality frameworks
• ERP/SAP or SaaS source integration

Experience and Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering or related discipline.
• Typically 3–5 years of experience in data engineering, data warehouse development or platform engineering.
• Hands-on experience with Snowflake or modern cloud data warehouse platforms.
• Strong SQL development skills and practical understanding of ELT/ETL pipeline patterns.
• Experience with Azure Data Factory, Python and Git-based development practices is preferred.
• Candidate Profile
• A strong candidate will be technically hands-on, disciplined in documentation and comfortable working through ambiguity. They should enjoy building reliable pipelines, understanding business context and improving engineering patterns so that data products can scale beyond one-off reporting requests.