About the position
Lead the design, development and operation of enterprise-grade data engineering solutions. Ensure the availability, quality, security and scalability of data pipelines and platforms, while mentoring engineers and embedding best practices aligned to enterprise data architecture and governance standards.
- Lead the design, build and maintenance of robust, scalable and reusable data pipelines for batch and near–real-time processing.
- Architect and oversee data ingestion, transformation and storage patterns across structured and semi-structured data sources.
- Translate business and analytical requirements into performant, secure and cost-effective data engineering solutions.
- Define and maintain detailed technical specifications for data sources, data flows, transformations, storage layers and downstream consumption.
- Ensure the production and upkeep of technical artefacts including source-to-target mappings, data models, pipeline documentation and data dictionaries.
- Collaborate with analytics, BI, architecture, governance and application teams to enable trusted, analytics-ready data.
- Champion enterprise data engineering standards, patterns and frameworks to ensure consistency, reliability and maintainability.
- Embed data quality, validation, lineage and observability into data pipelines by design.
- Ensure secure handling of live, sensitive and confidential data in line with governance, regulatory and compliance requirements.
- Assess and manage the impact of upstream system changes on data pipelines, platforms and downstream consumers.
- Optimise data platform performance, scalability and cost efficiency.
- Lead testing, monitoring, troubleshooting and incident resolution for data pipelines and platforms.
- Support the enablement of reporting, dashboards, analytics and advanced use cases through well-designed data foundations.
- Communicate delivery progress, risks and technical decisions clearly to technical and non-technical stakeholders.
- Proactively identify opportunities to improve data platform maturity, automation and engineering efficiency.
Minimum Requirements:
Knowledge:
Strong understanding of data engineering architectures and patterns.
Data warehousing and lakehouse concepts.
ETL / ELT design and orchestration.
Cloud data platforms and services.
Database design (relational and analytical).
Data modelling (dimensional, data vault, etc.)
SQL and performance optimisation.
Data quality, lineage and metadata management.
DevOps, CI/CD and infrastructure-as-code principles.
Software Development Lifecycle (SDLC).
Data governance, security and compliance principles.
Skills:
Strong data development, analysis and visualisation skills using tools (e.g. SQL, AWS, Databricks, Power BI, Qlik, etc.)
Cloud Platforms AWS, Microsoft, Snowflake or Databricks.
Strong hands-on experience with SQL and data pipeline tools.
Ability to translate business requirements into scalable engineering solutions.
Business Communication.
Problem Solving.
Desired Skills:
- SQL
- Cloud Platforms AWS
- Microsoft
