Manager, Platform Operations

Role summary

Platform strategy and architecture

• Define platform strategy and modernization roadmap; assess Microsoft Fabric, Delta Lake, Apache Iceberg, and Databricks Lakehouse capabilities.
• Architect and govern Azure Lakehouse Medallion layers on ADLS Gen2, Azure Databricks, and Azure Synapse Analytics.
• Ensure platform designs are scalable, secure, cost-optimized, aligned with enterprise architecture, and ready for AI/ML, real-time analytics, and operational integration.

Data engineering and operations

• Lead enterprise ETL/ELT pipeline design, development, testing, and production using Azure Data Factory and Databricks technologies such as PySpark, Spark SQL, and Delta Live Tables.
• Ingest data from Oracle Fusion, Oracle CC&B, Maximo, HES/MDMS, SAP, and IoT/sensor sources.
• Establish infrastructure-as-code, CI/CD, automated data quality testing, and Git version control standards.
• Monitor pipeline failures, data quality, performance, availability, and freshness against service levels.

Data modelling and AI/ML

• Govern dimensional, Data Vault 2.0, and semantic-layer modelling, including dbt, Azure Analysis Services, or Power BI datasets.
• Oversee modelling for asset management, metering and billing, GIS, field service, SCADA/grid operations, and customer data.
• Provide AI/ML infrastructure, feature stores, training pipelines, MLflow integration, governed training data, and model-serving integrations.

Governance and stakeholder partnership

• Apply FinOps practices including Databricks cluster sizing, auto-termination, Synapse cost management, ADLS storage tiers, and resource tagging.
• Embed quality checks, metadata cataloguing, lineage, access control, and privacy controls in platform engineering.
• Partner with analytics, AI, BI, governance, and business teams; report performance, quality KPIs, cost optimization, and roadmap progress.

Team leadership

• Lead and develop the data platform engineering team, including the Lead, Data Platform; Associate, Data Engineering; and Sr. Associate, Utility Data Modeler.
• Promote professional development, continuous improvement, integrity, transparency, collaboration, innovation, and clear communication.
• Evaluate emerging platform capabilities, including Microsoft Fabric, Delta Lake, Databricks AI/BI, and Azure OpenAI Service integrations.

Essential requirements

• Bachelor's degree in information technology or equivalent.
• 9 years of relevant working experience.
• Deep Azure data engineering expertise and capability to lead a data platform engineering function.

Desired requirements

• Master's degree in information technology or equivalent.
• 12 years of relevant working experience.

Competencies

• Data platform architecture, data engineering, ETL/ELT, data modelling, and platform performance management.
• Azure Synapse, Data Factory, ADLS, Databricks, and AI/data engineering automation tools.
• Platform strategy, roadmap development, cloud cost governance, data governance alignment, data quality partnership, stakeholder communication, and analytical value delivery.