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# Infrastructure Data Analytics Engineer

- **Company:** U.S. Bank
- **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, DevOps, Github, Monitoring of Systems, Identity and Access Management, JSON, Python (Programming Language), Meta-Data Management, Windows Servers, Operational Data Store, Systems Development Life Cycle, Release Management, Power BI, Runbook, 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, Integration Frameworks, Graphql, Api Design, Restful APIs, Splunk, Software Version Control, Data Pipelines, Dynatrace, Api Management, Servicenow, Alteryx, Databricks
- **Published:** September 25, 2026
- **Apply:** https://www.careerjet.com/job/usec966721143d658231f2859f37b4c325/eaa

## 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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