Key Responsibilities
Data platform design and delivery (platform-agnostic)
- Design and deliver modern data platforms using cloud-agnostic patterns (data lakes, lakehouses and data warehouses), supporting batch, streaming and near real-time processing.
- Build data ingestion, transformation and analytics pipelines, and enable data consumption for reporting, analytics and downstream applications.
- Support integration across cloud platforms and data technologies, aligning to client architecture, security and operating model requirements.
Architecture and platform standards (Architect focus)
- Define Modern Data Platform reference architectures and standards independent of vendor or cloud (including ingestion, modelling, governance, security and access).
- Design scalable, resilient and cost-optimised architectures and provide architectural oversight across delivery teams.
Data Engineering, Analytics & AI Enablement
- Develop and optimise data pipelines and transformations.
- Design analytical and reporting data models.
- Implement data quality, metadata, and lineage practices.
- Support advanced analytics and AI/ML enablement where required.
Operations, Governance & Optimisation
- Define monitoring, performance tuning, and cost management practices.
- Ensure data security, privacy, and compliance controls are applied.
- Support platform lifecycle management, CI/CD, and environment strategy.
- Contribute to platform documentation, standards, and operational runbooks.
Collaboration & Advisory
- Collaborate with business users, analysts, data scientists, and IT teams.
- Advise stakeholders on technology choices and architectural trade-offs.
- Mentor junior developers and consultants (Senior / Architect level).
Skills, Experience and Competencies
Technical Skills
- Strong understanding of modern data architectures (data lake, lakehouse and data warehouse), and experience integrating across cloud services and data ecosystems.
- Strong SQL, data modelling, and analytical design skills.
- Exposure to streaming, real-time, or event-driven data processing.
- Understanding of data governance, security, and cost optimisation.
Competencies
- Data product mindset, treating datasets, semantic models and reporting outputs as products with clear owners, consumers, quality measures and lifecycle management.
- Business-to-technical translation and collaboration, working with stakeholders and delivery teams to shape use cases and requirements, and translating them into practical Fabric design decisions and incremental delivery plans.
- Trusted data and quality by design building pipelines and models with validation, testing, lineage and reconciliation so teams can rely on data for analytics and AI-ready use cases.
- Standardise use shared patterns, templates, reference designs and accelerators to improve speed, quality and consistency across delivery.
- Iterative, outcome-driven delivery executing value in increments (ingestion → lake / lakehouse / warehouse → semantic model → reports), validates with users, and applies reusable patterns and automation to improve consistency.
Qualifications and Certifications
- Cloud data or analytics certifications (Azure, AWS, GCP).
- Platform specific certifications (Fabric, Databricks, Snowflake – desirable, Informatica- desirable).
- Architecture certifications (Architect level – desirable).
What Success Looks Like
- A scalable, governed, and future-proof modern data platform is in place.
- Data pipelines are reliable, performant, and easy to evolve.
- Data is trusted, secure, and widely adopted by business users.
- Analytics and insights are delivered faster and with higher quality.
- Platform costs and performance are actively optimised.
