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# AI Data Scientist / Analytics Engineer

- **Company:** Propertyvalue Team Red Dog
- **Location:** Redmond, United States (Remote available)
- **Experience:** Expert
- **Salary:** $149,760.0 - $164,320.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Business Analytics Applications, Data Analysis, Big Data, Business Systems, Cloud Computing, Information Systems, Computer Programming, Databases, Computer Engineering, Data Integration, Extract Transform Load (ETL), Dataspaces, Relational Databases, R (Programming Language), Python (Programming Language), Shell Script, SQL Databases, Unstructured Data, Enterprise Data Management, Data Processing, Cloud Platform System, Data Strategy, Information Technology, Data Analytics, Integration Frameworks, Data Pipelines, Programming Languages
- **Published:** July 17, 2026
- **Apply:** https://www.dice.com/job-detail/7b243e64-894c-4b4f-9ef4-534107a0f485

## About the Role

1. SQL and Enterprise Database Technologies - Deep experience building, optimizing, and supporting large-scale relational databases, enterprise data models, and high-performance analytics environments across complex business systems.
 2. ETL/ELT Pipeline Development and Data Integration - Strong expertise designing, automating, and maintaining scalable ETL/ELT workflows that support enterprise reporting, analytics, AI/ML, and cloud-based data platforms.
 3. Programming and Scripting (Python, R, Shell, etc.) - Hands-on experience using scripting and programming languages to automate data processing, support advanced analytics, troubleshoot data workflows, and improve operational efficiency.
 4. Cloud-Based Analytics and Enterprise Data Platforms - Experience working with modern cloud data ecosystems, large-scale datasets, and enterprise analytics environments to improve scalability, governance, reliability, and accessibility., Bachelor's degree in Computer Science, Computer Engineering, Data Science, Information Systems, or related technical field
8-10 years of experience in data science, analytics engineering, ETL development, database engineering, or related enterprise data roles
Advanced experience with SQL, relational databases, and enterprise database technologies
Strong hands-on experience building and supporting ETL/ELT pipelines and enterprise data integration workflows
Proficiency with Python, R, Shell scripting, or other programming languages used for analytics and automation
Experience working within cloud-based data platforms and enterprise analytics ecosystems
Strong analytical, troubleshooting, and problem-solving capabilities
Experience supporting large-scale datasets, advanced analytics initiatives, and enterprise reporting environments
Excellent written and verbal communication skills with the ability to communicate technical concepts to non-technical stakeholders
Ability to independently lead initiatives while collaborating effectively across technical and business teams

What makes a candidate highly successful in this role:
Highly successful candidates bring a combination of strong technical depth, enterprise-scale analytics experience, and the ability to collaborate effectively across technical and business teams. They are comfortable working with large, complex datasets, proactively identifying data quality or scalability challenges, and building sustainable solutions that improve operational efficiency and decision-making. Candidates who demonstrate strong ownership, strategic thinking, and experience supporting AI/ML or predictive analytics initiatives within cloud-based environments will stand out. Experience influencing cross-functional stakeholders and driving enterprise analytics modernization efforts is highly valued.

## Description

Partner with senior leadership, engineering teams, analysts, and business stakeholders to define enterprise analytics and data strategy initiatives
Design, develop, automate, and maintain scalable ETL/ELT pipelines and enterprise data workflows
Build and optimize logical and physical database designs, schemas, and enterprise data structures
Analyze structured and unstructured datasets to support business intelligence, predictive analytics, and AI/ML initiatives
Improve data quality, governance, accessibility, and operational reliability across enterprise systems
Optimize database systems, analytics platforms, and data processing solutions for scalability and performance
Troubleshoot complex data integration, performance, and data quality issues while implementing sustainable solutions
Support cloud-based analytics environments and enterprise data infrastructure modernization efforts
Create and maintain technical documentation for data workflows, governance standards, and enterprise analytics solutions
Collaborate cross-functionally to support data-driven business initiatives and strategic decision-making


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