AI Data Scientist / Analytics Engineer

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

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.

At Team Red Dog, people are at the heart of everything we do. Our commitment to personalized service and our deep experience in matching talented professionals with meaningful roles at some of the world’s most inspiring companies is what sets us apart. We take the time to understand your unique skills, strengths, and passions-because we believe your career should reflect who you are.

Whether you’re looking to grow, pivot, or simply find a place where your work truly matters, we offer opportunities that empower you to make a positive impact. With excellent benefits, a supportive team, and a role where you can thrive while doing what you love, we’re here to help you take the next step with confidence. Join us-and discover what it means to be genuinely valued in your career.

Generous benefits package for qualified employees includes: Health insurance (medical, dental, vision, and life) Employer-matched 401K plan Paid time off Paid holidays, We offer competitive compensation aligned with U.S. industry standards, and our final offer will reflect the candidate’s location, job-specific skills, experience, and knowledge. All applicants must be authorized to work in the U.S. without the need for sponsorship. Team Red Dog is an E-Verify employer. Employment is contingent upon the successful completion of a reference and background check. Please no solicitations from C2C or recruiting firms.

Team Red Dog is building a pipeline of qualified candidates for an anticipated Senior AI Data Scientist / Analytics Engineer opportunity with our client, a leading cloud and software provider and AI-driven technology organization. While this role is not officially open yet, it is expected to move forward soon and will focus on enterprise-scale analytics, ETL/ELT pipeline development, predictive modeling, and cloud-based data engineering initiatives that support advanced business intelligence and AI/ML solutions. The ideal candidate will work across large, complex datasets to deliver scalable data workflows, optimize analytics infrastructure, and generate actionable insights that influence strategic business decisions. This role offers the opportunity to partner closely with senior leadership, engineering teams, and cross-functional stakeholders while helping shape modern enterprise data ecosystems and next-generation analytics capabilities.