About the role
The candidate will have responsibilities across the following functions:
Data Platform Leadership:
- Lead the development and continuous improvement of data pipelines and integration frameworks.
- Define standards for ingestion, orchestration, and data movement across the platform.
- Improve reliability, performance, and cost efficiency of data pipelines and storage.
- Design scalable, reusable patterns that support consistent data flow across systems.
Data Integration and Architecture:
- Design and implement integrations across internal and external systems using tools such as Fivetran and custom pipelines.
- Own how data enters and moves through the warehouse, ensuring consistency and integrity.
- Structure data layers to support both upstream flexibility and downstream consumption.
- Ensure strong alignment between source system logic and warehouse representations.
Data Modelling and Foundation:
- Architect and implement core data models in Snowflake using dbt.
- Build durable data layers aligned to core revenue and operational domains.
- Ensure strong alignment between upstream data structures and downstream reporting needs.
- Embed testing, documentation, and performance optimisation into standard engineering practice.
Data Reliability and Quality:
- Establish and enforce data quality standards across ingestion and transformation layers.
- Design systems for validation, monitoring, and lineage.
- Proactively identify and resolve data issues, reducing ambiguity and downstream impact.
- Strengthen trust in enterprise data through consistent and reliable delivery.
AI-Enabled Data Systems:
- Treat AI as a core capability in modern data engineering, leveraging it to deliver high-quality solutions with greater speed and rigour.
- Design data pipelines and structures that enable safe and effective use of AI with trusted data.
- Integrate AI into engineering workflows to accelerate development, monitoring, and optimisation.
- Apply disciplined judgment when evaluating AI-generated outputs, ensuring enterprise standards for accuracy and consistency.
- Establish practical guardrails for responsible AI usage across both engineering and business consumption.
Cross-Functional Partnership:
- Translate complex and ambiguous business questions into scalable data solutions.
- Communicate technical trade-offs clearly to both technical and business stakeholders.
- Introduce platform and process improvements and drive them through implementation.
- Influence data standards across the Data and Analytics function.
Requirements:
- Ability to operate independently in complex, evolving environments.
- Clear and structured communication.
- Strong analytical reasoning and systems thinking.
- Ownership mindset with focus on measurable business outcomes.
- Willingness to challenge assumptions constructively.
- High standards for data quality, reliability, and engineering rigour.
Qualifications:
- 6+ years of experience in data engineering or analytics engineering.
- Strong experience with data integration, pipeline development, and warehouse architecture.
- Strong hands-on experience with Snowflake and dbt.
- Experience with data integration tools such as Fivetran.
- Experience working with cloud platforms such as AWS.
- Advanced SQL with demonstrated performance tuning expertise.
- Experience with Git-based workflows and GitHub for version control.
- Experience building and operating reliable data pipelines.
Preferred:
- Experience in SaaS or subscription-based businesses.
- Experience working with global stakeholders.
- Python for automation or platform tooling.