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Google Data Science Jobs in New Mexico (NOW HIRING)

Adapts instruction using Excel, Google Sheets, SQL platforms, and visualization tools like Tableau ... science to create personalized learning experiences. Through 1-on-1 Online Tutoring, students ...

Client Partner, Pharma

Santa Fe, NM · On-site

$160K - $180K/yr

... sciences, focused on SaaS, Real World Data, or services. * Deep understanding of life sciences ... Experience with Google suite of productivity applications (Sheets, Slides, Docs) as well as ...

GIS Manager

Albuquerque, NM · On-site

$81K - $109K/yr

Manage and oversee company ArcGIS Enterprise and data management system, including all GIS related ... Proficient in standard MS Office applications, Google Earth (KMZs and KMZ conversions)

Program Manager

Albuquerque, NM · On-site

$141K - $200K/yr

... or Google Cloud * Excellent communication, presentation, and stakeholder management skills ... scientific discovery. Our expertise in satellites, sensors and instruments, ground systems and data ...

Program Manager

Albuquerque, NM · On-site

$141K - $200K/yr

... or Google Cloud * Excellent communication, presentation, and stakeholder management skills ... scientific discovery. Our expertise in satellites, sensors and instruments, ground systems and data ...

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Showing results 1-20

Google Data Science information

See New Mexico salary details

$21.2K

$100.2K

$173.3K

How much do google data science jobs pay per year?

As of Aug 24, 2026, the average yearly pay for google data science in New Mexico is $100,205.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,960.00 and $125,760.00 per year, depending on experience, location, and employer.

What is a Google data science?

A Google Data Science job involves analyzing large datasets to provide insights and drive data-informed decisions. Data scientists at Google apply statistical modeling, machine learning, and analytical techniques to solve complex problems in products like Search, Ads, YouTube, and Cloud. They work closely with engineers, product managers, and business teams to develop data-driven solutions. Strong coding skills in Python or SQL, experience with big data tools, and a solid foundation in statistics are essential for this role.

What types of projects do Google data science professionals typically work on?

Google Data Science professionals engage in a wide variety of impactful projects, such as optimizing algorithms for product recommendations, improving user experiences through data-driven insights, and developing predictive models to inform business strategies. They often work closely with product managers, engineers, and designers to translate complex data findings into actionable solutions. The work environment is highly collaborative and fast-paced, with opportunities to contribute to innovative initiatives across different Google products and services. This dynamic setting allows data scientists to continuously expand their skill sets and take on new challenges, fostering both personal and professional growth.

What are the key skills and qualifications needed to thrive in the Google data science position, and why are they important?

To thrive as a Google Data Science professional, you need a strong foundation in statistical analysis, machine learning, and data manipulation, often supported by a degree in a quantitative field such as computer science, statistics, or mathematics. Proficiency in programming languages like Python or R, experience with large-scale data processing tools (such as SQL, TensorFlow, or BigQuery), and familiarity with cloud-based platforms are commonly required. Excellent problem-solving, communication, and collaboration skills help set candidates apart in effectively translating complex data insights to varied stakeholders. These capabilities are crucial for driving impactful, data-driven decisions within cross-functional teams at Google.

Infographic showing various Google Data Science job openings in New Mexico as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 11% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $100,205 per year, or $48.2 per hour.

IT Data Platform Data Engineer

Albuquerque, NM • On-site

PNM Resources
Utilities • 501 - 1,000 employees

$128K - $161K/yr

Full-time

Posted 11 days ago


Job description

POSTING DEADLINE
This position is posted until filled.
JOB DESCRIPTION
IT Data Platform Data Engineer
Salary Grade: G05
Minimum Midpoint Maximum
$94,831 - $128,022 - $161,213
The following statements are intended to describe the general nature and level of work being performed. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and skills.
SUMMARY
Under limited direction, leads the design, build, and maintenance of enterprise-scale data pipelines and data platforms that enable advanced analytics, reporting, and AI initiatives. Partners with business and IT leadership to shape strategic data requirements, ensure robust data integration, and deliver timely access to high-quality data across the organization. Champions data engineering best practices, drives governance and security compliance, and leads continuous improvement of enterprise data infrastructure.
ESSENTIAL DUTIES AND RESPONSIBILITIES.
Leads the design, development, and maintenance of automated data pipelines for ingesting, transforming, and storing complex structured and unstructured data from multiple evolving sources
Drives collaboration with BTS and business leadership to architect reliable, scalable enterprise data solutions supporting analytics, reporting, and AI use cases
Architects, builds, and oversees data integration processes, ETL/ELT workflows, and orchestration frameworks to ensure availability, performance, and resilience
Establishes and enforces data quality, completeness, accuracy, and timeliness through advanced validation, proactive monitoring, and remediation processes
Defines and implements sophisticated data models and structures optimized for enterprise analytics and advanced analysis
Guides strategy for cloud and on-premises data platforms, including data lakes, data warehouses, and next-generation analytics environments
Leads and mentors data analysts, data scientists, and junior engineers in feature engineering and model readiness
Develops and governs metadata management, lineage, and documentation standards while driving adoption across the enterprise
Owns compliance with data security, privacy, and regulatory requirements in alignment with enterprise policies
Leads performance tuning, capacity planning, and optimization efforts for data pipelines and data storage
Oversees platform health and proactively resolves complex, systemic data pipeline issues
Authors and maintains advanced data engineering designs, standards, and operational procedures
Exercises expert judgment in evaluating technical options, balancing cost, performance, scalability, security, and maintainability
Identifies and drives innovation opportunities to modernize and expand enterprise data platforms
COMPETENCIES
Expert understanding of enterprise data architecture, integration patterns, and large scale data platforms
Demonstrates mastery of SQL and expert proficiency in Python, data modeling, and data integration tools
Diagnoses and resolves complex data flows, system dependencies, and platform level issues
Evaluates technical solutions strategically, considering long term scalability, cost, performance, and risk
Champions innovation, challenges assumptions, and leads adoption of emerging technologies
Leads and influences across matrixed organizations, driving consensus and alignment
Facilitates enterprise coordination across data, analytics, engineering, and IT functions
Defines, institutionalizes, and enforces engineering standards, best practices, and architectural guidelines
Communicates persuasively with technical teams and executive audiences
Interprets complex technical and business documentation and converts it into actionable engineering plans
Rapidly acquires, unlearns, and instills advanced tools, frameworks, and engineering methodologies
Builds trust with internal partners and cross functional teams through credibility and technical authority
QUALIFICATIONS
Bachelors degree in Computer Science, Information Systems, Engineering, or related field, with seven to nine years of progressively responsible experience in data engineering, data integration, or analytics platforms, or an equivalent combination of advanced education and experience.
A masters degree or specialized certifications preferred
CERTIFICATES, LICENSES AND REGISTRATIONS (Preferred)
Microsoft Certified: Power BI Data Analyst Associate
Tableau Desktop Certified Associate
IBM Data Analyst Professional Certificate
SAS Certified Advanced Analytics Professional (Using SAS 9)
Data Science Council of America (DASCA)
Databricks Certified Data Analyst Associate
Google, AWS, Microsoft Azure Data Analytics certifications
WORK ENVIRONMENT AND PHYSICAL REQUIREMENTS
Office environment.
Travel approximately 10% of the time.
Ability to sit, stand, walk, and stoop as required.
Manual dexterity and good vision required.
Must occasionally lift and/or move up to 10 pounds.
SAFETY AND ADA STATEMENT
Safety Statement:
Safety is a core value at (TXNM Energy/PNM/TNMP) and our vision, "everyone goes home safe", reflects our commitment to promoting an environment conducive to learning, improving and building safety practices. Our safety value is built upon the belief that every employee deserves to work in an environment free from harm.
Americans with Disabilities Act (ADA) Statement:
If you require assistance with the job application process due to a disability, please contact HR ADA Analyst, at 505-241-4627.
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