The Organisation
Plan International is an independent development and humanitarian organisation that advances children’s rights and equality for girls. We believe in the power and potential of every child but know this is often suppressed by poverty, violence, exclusion and discrimination. And it is girls who are most affected.
Working together with children, young people, supporters and partners, we strive for a just world, tackling the root causes of the challenges girls and vulnerable children face. We support children’s rights from birth until they reach adulthood and we enable children to prepare for and respond to crises and adversity. We drive changes in practice and policy at local, national and global levels using our reach, experience and knowledge.
For over 85 years, we have rallied other determined optimists to transform the lives of all children in more than 80 countries.
We won’t stop until we are all equal.
Role Purpose
The Snowflake Data/DBT Engineer is responsible for designing, developing, maintaining and optimising Plan International’s cloud-based data platform and data integration solutions. The role delivers scalable, secure, resilient and high-performing data pipelines and data products that enable trusted reporting, analytics, business intelligence and data-driven decision-making across the organisation. Working within the global Data & Analytics function, the role focuses on building and supporting modern ELT processes using Snowflake, dbt and SQL, integrating data from SAP S/4HANA and other enterprise systems into a governed enterprise data platform.
The position holder ensures that data engineering solutions are reliable, performant, well modelled, observable, secure, cost-effective and maintainable. The role contributes to data quality, governance, metadata, lineage and data security requirements and supports the continual evolution of Plan International’s modern cloud data architecture. The position holder collaborates closely with business stakeholders, Data Architects, Reporting & Analytics teams, Enterprise Architecture, IT, source-system owners and implementation partners to develop robust, reusable and maintainable data engineering solutions supporting Plan International’s global operations across more than 80 countries.
Accountabilities
Snowflake Data Engineering
- Design, develop and maintain scalable ELT pipelines using Snowflake, dbt and SQL.
- Build reusable, modular and well-tested dbt models following enterprise engineering standards.
- Develop high-performance analytical and dimensional data models that support enterprise reporting, analytics and self-service BI.
- Apply appropriate Snowflake optimisation techniques, including effective data modelling, micro-partition pruning, clustering where appropriate, warehouse sizing, workload management and query optimisation.
- Design and implement incremental processing, change data capture and historisation strategies appropriate to business and technical requirements.
- Develop reusable engineering patterns, frameworks, macros and packages to improve development efficiency and consistency.
- Implement robust error handling, monitoring, alerting and automated recovery processes within data pipelines.
- Ensure data engineering solutions are scalable, secure, resilient, maintainable and aligned with enterprise architecture standards.
- Apply appropriate Snowflake capabilities, including dynamic tables, streams, tasks and other platform services where they provide an effective solution to business requirements.
Data Integration & ETL Development
- Develop and support enterprise ETL/ELT solutions integrating SAP S/4HANA and other enterprise source systems into Snowflake.
- Design and maintain data ingestion and integration processes using SNP Glue and associated technologies.
- Apply appropriate integration and orchestration patterns, including scheduled, event-driven and API-based processing where required.
- Develop incremental loading, CDC and automated data validation processes.
- Investigate and resolve data integration issues across multiple platforms, source systems and environments.
- Ensure data quality, completeness, consistency and integrity throughout the data lifecycle.
- Investigate and reconcile data between SAP S/4HANA and Snowflake to identify discrepancies, data-quality issues and integration defects.
- Collaborate with source-system owners to understand data structures, business processes, source-system changes and integration requirements.
- Assess the impact of upstream system changes on downstream data pipelines and analytical datasets.
- Maintain appropriate technical documentation for source-to-target mappings, integration processes and transformation logic.
Data Modelling & Analytics Enablement
- Design and implement scalable dimensional and analytical data models aligned with enterprise data architecture principles.
- Apply appropriate approaches to data grain, keys, historisation, slowly changing dimensions and reusable/conformed business entities.
- Develop trusted, reusable datasets to support Power BI, Microsoft Fabric and other analytical and reporting platforms.
- Work with Reporting & Analytics teams to ensure datasets are performant, appropriately structured and fit for analytical consumption.
- Ensure transformation logic is transparent, documented and traceable from source through to consumption.
- Support the development of reusable semantic and business data structures where required.
- Ensure data models support both current reporting requirements and future analytical use cases.
DevOps & Platform Operations
- Support DevOps practices including CI/CD pipelines, automated deployments and source-code management.
- Maintain version-controlled dbt projects and deployment processes across development, test and production environments.
- Implement automated validation and testing as part of the development and deployment lifecycle.
- Apply appropriate unit, integration, data-quality and regression testing practices.
- Participate in release planning, deployment activities and production support.
- Contribute to Infrastructure as Code and automated provisioning practices where applicable.
- Monitor platform health, investigate incidents and implement continuous improvements to operational performance
- Develop and maintain technical documentation for engineering solutions, deployment processes and operational procedures
- Establish and maintain appropriate development, testing and production controls.
Collaboration & Solution Delivery
- Work closely with Data Architects and business stakeholders to translate business requirements into scalable technical solutions.
- Collaborate with source-system owners to understand business processes, data structures and integration dependencies.
- Support Reporting & Analytics teams by delivering trusted, well-modelled datasets for Power BI, Microsoft Fabric and other analytical platforms.
- Participate in solution design, technical estimation, planning, testing and implementation activities.
- Provide technical input into architecture and design decisions.
- Communicate technical risks, dependencies and recommendations clearly to technical and non-technical stakeholders.
- Support User Acceptance Testing and production releases where required.
- Work effectively with geographically distributed teams across Plan International’s global operating environment.
- Collaborate with external implementation partners and technology vendors while maintaining appropriate internal technical knowledge and ownership.
Snowflake Platform, Security & Cost Optimisation
- Develop and maintain Snowflake databases, schemas, warehouses and supporting platform configurations in accordance with enterprise standards.
- Monitor Snowflake workloads, resource utilisation, performance and platform health.
- Optimise Snowflake compute and storage consumption through appropriate warehouse sizing, workload management, query optimisation and data lifecycle practices.
- Identify opportunities to improve platform efficiency and reduce unnecessary cloud data costs.
- Implement and maintain role-based access controls and least-privilege principles.
- Support appropriate implementation of data masking, access policies and other Snowflake security capabilities.
- Ensure Snowflake environments are appropriately configured across development, test and production.
- Contribute to platform capacity planning and performance management.
- Support the implementation of platform resilience, disaster recovery and business continuity requirements.
- Work with Enterprise Architecture, IT and technology vendors to ensure the Snowflake platform evolves in line with Plan International’s strategic technology direction.
Production Support, Reliability & Resilience
- Monitor critical data pipelines, Snowflake workloads and associated integration processes.
- Provide Level 2/3 technical support for data engineering services and production data pipelines.
- Investigate incidents, perform root-cause analysis and implement effective remediation.
- Ensure recurring production issues are addressed through preventative engineering improvements.
- Support agreed availability, reliability and data-freshness SLAs/SLOs for critical data services.
- Participate in incident, problem and change management processes.
- Contribute to disaster recovery, backup/recovery and business continuity activities.
- Ensure operational procedures, runbooks and support documentation are maintained.
- Participate in out-of-hours support arrangements where required by the business.
Continuous Improvement
- Identify opportunities to improve platform performance, reliability, automation and engineering efficiency.
- Promote reusable development patterns, automation and engineering best practice.
- Identify opportunities to simplify and standardise data engineering processes and solutions.
- Evaluate emerging Snowflake, dbt, cloud and data engineering technologies and recommend improvements where appropriate.
- Contribute to the continual evolution of Plan International’s modern cloud data architecture.
- Share knowledge and mentor other members of the Data & Analytics function where appropriate.
- Promote engineering quality, automation, documentation, testing and operational excellence.
- Contribute to the development and continual improvement of enterprise data engineering standards.
Technical expertise, skills and knowledge required
