Senior Data Engineer

The Team

The Senior Data Engineer – Marketing Technology reports to the MarTech Data & Insights Manager within the Marketing Technology Team, which is a graphically dispersed APAC-wide team of 14 members. It is important that this role establishes a strong connection with:

Internal Stakeholders: Marketing, Data Services, AI & Ontology Team, BI/Reporting teams, Strategy and Product teams, Infrastructure teams (DBA, SysEng and DevOps), Development teams

External Stakeholders: Databases housed outside of SEEK

The Role

The Senior Data Engineer is a vital part of the Marketing Technology Team, working closely with the MarTech Data & Insights Manager and Data Team to shape the data structures and models that underpin marketing technology. You will translate marketing and customer requirements into clear, trusted and scalable data models that support segmentation, personalisation, analytics and activation across ANZ and Asia.

This is a pivotal, data-modelling-focused role responsible for analysing source systems, defining conceptual, logical, physical and dimensional models, and partnering with data engineers to implement them through robust pipelines. You will establish the right grain, keys, relationships, business definitions and data quality expectations, balancing usability, performance, scalability, security and long-term maintainability. Experience with Databricks is welcome but not required

Key Responsibilities

• Work with key stakeholders, business analysts, data analysts and development teams to understand requirements and define fit-for-purpose data modelling solutions.
• Lead the design and documentation of conceptual, logical, physical and dimensional data models for data warehouses, datamarts and analytical use cases.
• Define entities, attributes, relationships, keys, grain, hierarchies and business definitions in partnership with subject matter experts.
• Design and evolve scalable data warehouse, datamart and analytical schemas that support consistent reporting, segmentation, personalisation and activation.
• Analyse source systems and perform source-to-target mapping, identifying data meaning, dependencies, gaps and transformation requirements.
• Partner with data engineers to implement models through reliable ETL/ELT and data processing pipelines.
• Use advanced SQL to validate models, transformations and data quality, and to support data discovery and troubleshooting.
• Apply modelling standards and contribute to consistent naming, documentation, metadata, lineage and governance practices
• Review and optimise model performance, query efficiency, physical schemas and underlying system resource utilisation.
• Support end users of data assets by helping them find, understand and use trusted data, and by resolving modelling or data quality issues.
• Identify and resolve data quality issues by working with source-system owners and delivery teams.
• Contribute to operational support of data systems, including incident response, monitoring and alerting, CI/CD and relevant database administration.
• Understand the marketing, sales and service customers your team supports, using their needs to guide modelling priorities and maximise business impact across APAC.
• Drive continuous improvement in the data modelling and engineering process, communicating progress, risks and changes that may affect stakeholders.
• Contribute to an environment of collaboration, knowledge sharing and continuous learning within the team