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Strong employer brand, metro location, mid-level generalist data role with broad skills requirements.
Data platform and cloud skills are transferable across industries, but enterprise data governance experience increases specificity.
Explicit 4–9 years plus mandatory Databricks, GCP, Python, SQL, Airflow, and Power BI requirements.
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Design and own end-to-end scalable, modular data and analytics architectures spanning Azure and GCP platforms, including data lakes, warehouses, BI, web applications, and AI-ready solutions.
Architect, optimise, and standardise data pipelines and workflows to ensure reliability, maintainability, and cost-efficiency aligned with Data Foundation standards.
Lead AI & advanced analytics enablement by designing AI-ready data models, implementing agentic AI patterns, and enforcing AI governance in enterprise systems.
4 to 9 years of relevant experience in data solution architecture and analytics product development.
Strong hands-on expertise in Python, Databricks ecosystem (Delta Lake, Unity Catalog), SQL/PostgreSQL, and Apache Airflow.
Proven experience with Google Cloud Platform (Cloud Run, architectures) and Azure ecosystem (ADF, Azure SQL, MS SQL Server).
Work Experience Required: 4 to 9 years in data solution architecture roles as explicitly stated.
Technical leader with strong cross-cloud (Azure + GCP) and multi-disciplinary skills in data architecture, advanced analytics, AI/agentic AI frameworks (LangChain, AutoGen), and scalable data product delivery.
Experienced in leading architecture design and governance in enterprise-scale, data-intensive or digital-first environments with exposure to data lakes, BI platforms, and AI product lifecycle.
Practitioner with hands-on expertise in software engineering, microservices, workflow orchestration, and data governance to translate business needs into robust, maintainable production systems.