Sr Data Engineer, Data Analytics & Intelligence, NA

Vantage Data Centers powers, cools, protects and connects the technology of the world’s well‑known hyperscalers, cloud providers and large enterprises. Across North America, EMEA and Asia Pacific, the company has evolved data center design in innovative ways to deliver dramatic gains in reliability, efficiency and sustainability in flexible environments that can scale as quickly as the market demands.

Within Operational Excellence, the Data Analytics & Intelligence function enables Operations to move from reactive reporting to proactive, insight‑driven execution. The team builds trusted data foundations, governed KPI frameworks, operational intelligence products and AI‑ready data assets that support performance visibility, decision‑making, predictive insights and scalable operational excellence across North America.

This position will be based on‑site in Denver, CO, with three days on site required and two days flexible in alignment with the flexible work policy.

Vantage Data Centers is seeking a Sr Data Engineer to help build, operate and scale the governed data foundation for Operations, North America. The role is designed for an engineer who can independently deliver production‑ready pipelines, curated datasets, semantic‑model inputs and AI‑ready data products that support reporting, executive insight preparation and the emerging AI Insight Solution.

• Design, build and maintain reliable, scalable data pipelines using Python and PySpark on the Microsoft Azure data platform.
• Develop and operate batch and incremental data pipelines leveraging Azure Data Factory for orchestration and Azure Data Lake Storage Gen2 as the primary data store.
• Build and maintain curated lakehouse / gold‑layer datasets and semantic‑model inputs that support governed operational insights and AI‑enabled consumption.
• Independently implement SQL‑ and Spark‑based transformations to produce curated datasets that support enterprise reporting, analytics, AI‑enabled insight preparation and downstream consumption.
• Take ownership of assigned data pipelines and datasets, including monitoring, troubleshooting, performance optimisation, documentation and production support.
• Work with Azure Synapse, Microsoft Fabric / Lakehouse patterns where applicable, and related Azure analytics services to support analytical workloads and data consumption patterns.
• Prepare structured operational data for AI‑enabled use cases by documenting business rules, source lineage, data reliability constraints, known quality limitations and data dictionary definitions.
• Support source visibility, confidence context, and Data Reliability & Trust Indicator integration where applicable so downstream analytics and AI outputs can be understood and trusted.
• Contribute to ontology, taxonomy, semantic model and data dictionary alignment needed to connect operational context, KPIs, incidents, work orders and other enterprise data domains.
• Collaborate with business analysts, operations SMEs, data stewards, IT Global and cross‑functional stakeholders to translate requirements into practical, working data solutions.
• Apply established data governance, security, access‑control, data classification and engineering standards to ensure compliant, maintainable and scalable solutions.
• Identify, document and route data‑quality issues to accountable owners, helping improve source correction rather than masking defects downstream.
• Participate in code reviews, technical discussions, sprint planning and platform improvement initiatives as an active contributor.
• Proactively identify data quality issues, pipeline risks, platform dependencies and improvement opportunities, and communicate them clearly in a fast‑paced environment.

• Develop and maintain PySpark notebooks and jobs to ingest, transform, validate and curate data within the enterprise data platform.
• Build and modify Azure Data Factory pipelines for batch and incremental data processing.