Technical Delivery Lead
Visionet Systems Inc.
UK
5 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours
Job source
Tech stack
Big Data
Information Engineering
Data Governance
Data Integration
Data Warehousing
Performance Tuning
Cloud Services
Apache Spark
Microsoft Fabric
Pyspark
Performance Monitor
Data Management
+1 more
Data Pipelines
Job description
Responsible for the full lifecycle of enterprise-scale data integration and analytics platform activation on Microsoft Fabric within a large, complex marketing analytics environment. This role designs, builds, and operationalizes robust data pipelines, transformations, and data models and manages controlled, production-grade releases to ensure platform stability, scalability, and trusted analytics consumption., * Design, govern, and operationalize Fabric pipelines, PySpark transformations, and Lakehouse-based Medallion architecture.
- Translate business requirements and approved data roadmaps into executable technical designs, sprint backlogs, and production-grade delivery plans.
- Own platform performance, reliability, and scalability through proactive monitoring, Spark performance tuning, and workload optimization.
- Act as the technical coordination point across business, governance, IT, and analytics engineering teams to deliver certified, production-ready analytical datasets.
- Oversee release engineering including Dev-Test-Prod promotion, production cutovers, rollback strategies, and post-release stabilization of pipelines and models.
- Provide technical leadership through architecture reviews, code governance, reusable framework development, and long-term scalability planning for the platform.
Requirements
- Minimum 10-15 years in data engineering, analytics delivery, or large-scale data platform execution roles.
- Strong experience with cloud data platforms (Microsoft Fabric), large-scale ingestion & transformation pipelines, and data warehousing / lakehouse concepts.
- Familiarity with data governance, compliance, and data quality frameworks.
- Excellent stakeholder coordination across business, IT, governance, and analytics functions.
- Hands-on technical proficiency in data pipeline tools, data modelling, and data product delivery.
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