Sr Data Engineer, Data Analytics & Intelligence, NA
Vantage Data CentersDenver, CO
Full Time
Posted July 15, 2026
$130k - $155k
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Listing evidence and provenance
- Source
- Workday View original listing
- Freshness
- Open in the current public read path; this is not an employer guarantee.Last observed October 5, 2026
- Classification
- Modern power-system classification not yet published for this listing.Legacy source industry field: Climate Technology
- Pay provenance
- Employer-provided pay
Job Description
Within Operational Excellence, the Data Analytics & Intelligence function enables Operations to move from reactive reporting to proactive, insight-driven execution.
Key Responsibilities
- 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 ingestion.
- Implement Spark-based transformations that write curated datasets to Azure Data Lake Storage Gen2 and/or Fabric Lakehouse patterns using established folder structures, naming conventions, and governance standards.
- Create and maintain SQL views, tables, lakehouse objects, and semantic-model inputs to support analytics, operational intelligence, and AI-enabled consumption patterns.
- Prepare datasets for Fabric Data Agent / AI agent use cases by documenting business rules, joins, grain, quality limitations, source lineage, and operational definitions.
- Respond to pipeline failures, data validation issues, operational alerts, and data-quality escalations with clear root-cause analysis and practical remediation steps.
- Perform performance tuning of Spark jobs and SQL workloads, including partitioning, filtering, incremental logic, query optimization, and resource-aware design within established architectural patterns.
- Validate data outputs with business partners, operations SMEs, and data stewards, and address defects or discrepancies through documented correction paths.
- Support observability, logging, and auditability practices for data pipelines and AI-consumable datasets where applicable.
- Commit code using Git, follow branching standards, participate in pull request reviews, and support CI/CD ways of working using GitHub, Azure DevOps, or similar tools.
- Update documentation for pipelines, datasets, data contracts, data dictionaries, business rules, and operational runbooks as changes are made.
- Execute assigned backlog items within sprint timelines and raise risks, dependencies, or blockers early.
- Additional duties as assigned by management.
Requirements
- Experience working with distributed data processing frameworks, including Apache Spark.
- Experience preparing governed data products for AI-enabled use cases, including Microsoft Fabric Lakehouse, semantic models, Fabric/Data Agent patterns, ontology or taxonomy alignment, and explainable AI outputs.
- Familiarity with data observability, metadata management, lineage, data contracts, reliability indicators, and operational best practices in production environments.
- Familiarity with additional Azure services such as Azure Functions or Logic Apps in support of data workflows.
- Experience supporting data platform enhancement, refactoring, modernization, or reusable architecture initiatives.
- Experience working with structured and unstructured operational sources, such as enterprise applications, operational workflows, documents, dashboards, and knowledge assets.
- Experience working in a scaling or fast-paced organization where priorities evolve quickly and practical delivery discipline is required.
- While performing the duties of this job, the employee is occasionally required to stand; walk; sit; use hands to handle, or feel objects; reach with hands and arms; climb stairs; balance; stoop or kneel; talk and hear. The employee must occasionally lift and/or move up to 25 pounds.
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