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# Infrastructure Data Analytics Engineer

- **Company:** U.S. Bank, National Association
- **Location:** Hopkins, MN, United States
- **Experience:** Expert
- **Salary:** $105,400.0 - $124,000.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Amazon Web Services, Business Analytics Applications, Data Analysis, Application Release Automation, Audit Trail, Automation of Tests, Microsoft Azure, Software as a Service, Cloud Computing, Configuration Management Databases, Computer Programming, Databases, Continuous Integration, Data Auditing, Data Cleansing, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Transformation, Data Mining, Decision Support Systems, Linux, DevOps, Github, Monitoring of Systems, Identity and Access Management, JSON, Python (Programming Language), Meta-Data Management, Windows Servers, Networking Basics, Operational Data Store, Systems Development Life Cycle, Release Management, Power BI, Software Deployment, Software Engineering, SQL Stored Procedures, SQL Databases, Systems Integration, Tableau (Software), Unstructured Data, Extensible Markup Language (XML), Enterprise Data Management, Datadog, Data Processing, Freeform SQL, Data Ingestion, Delivery Pipeline, Snowflake, Gitlab, Data Analytics, Graphql, Api Design, Restful APIs, Splunk, Software Version Control, Data Pipelines, Dynatrace, Api Management, Servicenow, Alteryx, Databricks
- **Published:** August 11, 2026
- **Apply:** https://usbank.wd1.myworkdayjobs.com/US_Bank_Careers/job/Hopkins-MN/Infrastructure-Data-Analytics-Engineer_2026-0020380-1

## About the Role

The ideal candidate possesses strong technical skills in SQL, Python, Alteryx, API integration, and data transformation while adhering to software development lifecycle (SDLC) practices, including source code management, testing, deployment, and release management., * Bachelor's degree in a related field, or equivalent work experience

 * Five to seven years of statistical and/or data analytics experience

Preferred Qualifications

 * Experience analyzing infrastructure, cloud, or operational technology data.
 * Experience with Power BI, Tableau, or similar visualization platforms.
 * Experience with Azure, AWS, Databricks, Snowflake, or enterprise data platforms.
 * Knowledge of Infrastructure Observability and Monitoring platforms (Datadog, Splunk, Dynatrace, ServiceNow, etc.).
 * Experience with CI/CD tools and release automation.
 * Understanding of data governance, metadata management, and data quality frameworks.
 * Experience supporting enterprise-scale transformation or modernization programs.

Technical Skills

Programming & Analytics

 * SQL (Advanced)
 * Python
 * Alteryx
 * Power BI
 * Excel

Data & Integration

 * REST APIs
 * JSON / XML
 * ETL / ELT
 * Data Modeling
 * Data Quality Management

DevOps & SDLC

 * GitHub / GitLab / Azure DevOps
 * Version Control
 * CI/CD Pipelines
 * Release Management
 * Test Automation
 * Agile & Scrum Methodologies

Infrastructure Knowledge (Preferred)

 * Azure
 * AWS
 * Windows Server
 * Linux
 * Networking Fundamentals
 * CMDB / Asset Management
 * Infrastructure Monitoring Platforms

## Description

The Infrastructure Data Analytics Engineer is responsible for acquiring, transforming, integrating, and analyzing data from infrastructure, platform, cloud, and enterprise technology systems. This role combines data engineering, analytics, automation, and operational intelligence to provide actionable insights that support technology strategy, operational excellence, risk management, and executive decision making., Data Acquisition & Integration

 * Design and develop data ingestion processes from multiple sources, including:
 + Infrastructure monitoring platforms
 + CMDB and asset management systems
 + Cloud platforms (Azure, AWS)
 + Enterprise databases
 + REST and GraphQL APIs
 + SaaS and third-party technology platforms
 * Build and maintain scalable ETL/ELT pipelines to acquire, cleanse, transform, validate, and enrich data.
 * Integrate structured and unstructured data from disparate technology systems into centralized analytics platforms.
 * Automate recurring data collection and processing activities.

Data Engineering & Transformation

 * Develop complex SQL queries, stored procedures, views, and data models.
 * Create Python-based solutions for:
 + Data extraction
 + Data transformation
 + Data quality validation
 + Automation workflows
 + API integrations
 * Design and maintain Alteryx workflows for data preparation, blending, and analytics automation.
 * Implement reusable transformation frameworks and standardized data processing patterns.
 * Perform data reconciliation and data quality assurance activities.

Analytics & Reporting

 * Analyze infrastructure and operational data to identify:
 + Trends
 + Risks
 + Performance issues
 + Capacity constraints
 + Optimization opportunities
 * Support executive reporting, operational scorecards, and KPI dashboards.
 * Translate technical findings into business-friendly recommendations and insights.
 * Partner with infrastructure, engineering, operations, and leadership teams to support data-driven decision making.

Automation & API Development

 * Develop API integrations between internal and external platforms.
 * Build automated workflows that reduce manual effort and improve data timeliness.
 * Support near real-time and batch data processing requirements.
 * Create reusable libraries and utilities that accelerate analytics delivery.

SDLC, DevOps & Release Management

 * Follow established Software Development Lifecycle (SDLC) methodologies including Agile delivery practices.
 * Maintain source code in approved repositories (GitHub, GitLab, Azure DevOps, etc.).
 * Utilize branching, pull request, peer review, and merging standards.
 * Develop and maintain CI/CD deployment pipelines.
 * Create and maintain technical documentation, runbooks, and deployment procedures.
 * Participate in release planning, change management, testing, and production deployments.
 * Ensure appropriate version control, auditability, and governance of analytics assets and code.
 * Support incident management and post-release validation activities.

Governance & Compliance

 * Ensure adherence to enterprise data governance, security, and compliance requirements.
 * Maintain data lineage and metadata documentation.
 * Implement controls for data quality, access management, and operational resiliency.
 * Support regulatory and audit requests related to analytics solutions.


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