Lead, Data Platform
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Key skills for this role
About the Role
TAQA Distribution is seeking a Lead, Data Platform to lead the technical design, development, and maintenance of the data platform. The role involves building data pipelines, data models, and infrastructure using Azure and Databricks, and mentoring junior team members.
Key Skills for This Role
Responsibilities
- Lead the design, development, and maintenance of TAQA's data platform infrastructure, including data lake storage (Azure ADLS), data warehouse/lakehouse compute (Azure Synapse, Databricks, or equivalent), and data ingestion frameworks.
- Design, build, and optimise complex data pipelines and ETL/ELT workflows using modern data engineering tools (Apache Spark, dbt, Azure Data Factory, or equivalent), ensuring data flows reliably from source systems to the data platform.
- Lead data modelling activities for the data platform, designing dimensional models, data vault structures, and semantic layer definitions that support accurate and performant analytical consumption.
- Implement data platform best practices including infrastructure as code (Terraform, CloudFormation), CI/CD for data pipelines, data testing frameworks, and data lineage tracking.
- Monitor data platform performance and data pipeline health, proactively identifying and resolving pipeline failures, data quality issues, and performance bottlenecks.
- Provide technical guidance and mentoring to the Associate, Data Engineering and Sr. Associate, Utility Data Modeler, reviewing code, setting engineering standards, and building team capability.
- Collaborate with the data governance team to ensure data platform outputs comply with data quality standards, metadata requirements, and data privacy controls.
- Apply AI powered data engineering tools, automated pipeline testing, and intelligent data quality monitoring to improve platform reliability and engineering productivity.
Requirements
- Bachelor’s degree in Information Technology or equivalent.
- 6 years of relevant working experience.
- Advanced to Expert proficiency in technical competencies: Data Pipeline Development, Data Lake & Data Warehouse/Lakehouse Architecture, Data Modelling, DataOps & CI/CD, Data Quality Monitoring & Lineage Tracking, AI & Data Engineering Automation.
- Intermediate to Advanced proficiency in business competencies: Data Platform Architecture Alignment, Data Governance & Quality Partnership, Stakeholder Communication & Technical Documentation, Continuous Improvement & Engineering Excellence.
Full Job Posting
Job Summary
- To lead the technical design, development, and maintenance of TAQA Distribution's data platform, ensuring data pipelines, data models, and data storage environments are built to high engineering standards and deliver reliable, high quality data for analytics, AI/ML, and reporting.
- The Lead, Data Platform acts as the senior data engineering authority within the data platform team, working under the direction of the Manager, Data Platform to deliver scalable data infrastructure and guiding the Associate, Data Engineering and Sr. Associate, Utility Data Modeler in their technica
Job Specific Responsibilities
- Lead the design, development, and maintenance of TAQA's data platform infrastructure, including data lake storage (Azure ADLS), data warehouse/lakehouse compute (Azure Synapse, Databricks, or equivalent), and data ingestion frameworks.
- Design, build, and optimise complex data pipelines and ETL/ELT workflows using modern data engineering tools (Apache Spark, dbt, Azure Data Factory, or equivalent), ensuring data flows reliably from source systems to the data platform.
- Lead data modelling activities for the data platform, designing dimensional models, data vault structures, and semantic layer definitions that support accurate and performant analytical consumption.
- Implement data platform best practices including infrastructure as code (Terraform, CloudFormation), CI/CD for data pipelines, data testing frameworks, and data lineage tracking.
- Monitor data platform performance and data pipeline health, proactively identifying and resolving pipeline failures, data quality issues, and performance bottlenecks.
- Provide technical guidance and mentoring to the Associate, Data Engineering and Sr. Associate, Utility Data Modeler, reviewing code, setting engineering standards, and building team capability.
- Collaborate with the data governance team to ensure data platform outputs comply with data quality standards, metadata requirements, and data privacy controls.
- Apply AI powered data engineering tools, automated pipeline testing, and intelligent data quality monitoring to improve platform reliability and engineering productivity.
Essential Requirements
- Bachelor’s degree in Information Technology or equivalent
- 6 years of relevant working experience
Desired Requirements
- Master’s degree in Information Technology or equivalent
- 9 years of relevant working experience
Technical Competencies
- Data Pipeline Development (Apache Spark, dbt, Azure Data Factory)
- Data Lake & Data Warehouse/Lakehouse Architecture (Azure ADLS/Synapse, Databricks)
- Data Modelling (Dimensional, Data Vault, Semantic Layer)
- DataOps & CI/CD for Data Pipelines (Terraform, GitHub Actions)
- Data Quality Monitoring & Lineage Tracking
- AI & Data Engineering Automation Tool Proficiency
Strategic & Business Competencies
- Data Platform Architecture Alignment
- Data Governance & Quality Partnership
- Stakeholder Communication & Technical Documentation
- Continuous Improvement & Engineering Excellence
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