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Data Analytics Engineer Jobs in Colorado (NOW HIRING)

BillGO, Inc.

AI Data Analytics Engineer

Fort Collins, CO · On-site

$113K - $135K/yr

We are hiring an AI Data Analytics Engineer to design, build, and ship the data, analytics, and AI capabilities that power BillGO's AI/Data Platform, from trusted data models and self-serve analytics ...

BillGO, Inc.

AI Data Analytics Engineer

Fort Collins, CO · On-site

$102K - $146K/yr

We are hiring an AI Data Analytics Engineer to design, build, and ship the data, analytics, and AI capabilities that power BillGO's AI/Data Platform, from trusted data models and self-serve analytics ...

Propelus

Senior Data Analytics Engineer

Boulder, CO · On-site +1

$109K - $149K/yr

As an Analytics Engineer, you will play a crucial role in transforming raw data into actionable insights to support data-driven decision-making across the company. You will work independently on ...

CoorsTek, Inc.

Forward Deployed Data Engineer

Golden, CO · On-site

$118K - $142K/yr

Identify high-value opportunities for data, analytics, AI, or workflow enablement by partnering with plant leaders, engineers, quality, supply chain, maintenance, finance, and business leaders.

CoorsTek

Forward Deployed Data Engineer

Golden, CO

$118K - $142K/yr

Identify high-value opportunities for data, analytics, AI, or workflow enablement by partnering with plant leaders, engineers, quality, supply chain, maintenance, finance, and business leaders.

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Showing results 1-20

All JobsData Analytics Engineer JobsData Analytics Engineer Jobs in Colorado

Data Analytics Engineer information

See Colorado salary details

$46.8K

$136.4K

$186.6K

How much do data analytics engineer jobs pay per year?

As of Aug 5, 2026, the average yearly pay for data analytics engineer in Colorado is $136,399.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,400.00 and $144,600.00 per year, depending on experience, location, and employer.

How do data analytics engineers typically collaborate with data scientists and business stakeholders on projects?

Data Analytics Engineers play a crucial role in bridging the gap between raw data and actionable insights by building, optimizing, and maintaining data pipelines. They often work closely with data scientists to ensure data is clean, accessible, and structured for advanced analytics or machine learning models. Additionally, they collaborate with business stakeholders to understand reporting requirements and ensure that data solutions align with organizational objectives. Regular communication and cross-functional teamwork are essential aspects of this role, as engineers must translate business needs into technical specifications and deliver reliable data products.

What are the key skills and qualifications needed to thrive as a data analytics engineer, and why are they important?

To thrive as a Data Analytics Engineer, you need strong proficiency in data modeling, SQL, and statistical analysis, typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools such as Python, R, Apache Spark, Tableau, and cloud data platforms like AWS or Google BigQuery is essential, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills help you translate data insights into actionable business solutions. These skills and qualities are crucial for designing robust data pipelines and enabling data-driven decision-making across organizations.

What is the difference between Data Analytics Engineer vs Data Scientist?

AspectData Analytics EngineerData Scientist
CredentialsBachelor's or master's in CS, Data Science, or related fields; certifications like Google Data AnalyticsBachelor's or master's in CS, Statistics, or related fields; certifications like Certified Data Scientist
Work EnvironmentFocus on building data pipelines, dashboards, and analytics toolsFocus on statistical modeling, machine learning, and data exploration
Employer & Industry UsageUsed across tech, finance, healthcare for data infrastructure and analyticsCommon in research, product development, and advanced analytics teams

While both roles work with data, Data Analytics Engineers primarily develop data infrastructure and tools for analysis, whereas Data Scientists focus on statistical modeling and machine learning to generate insights. They often collaborate but have distinct technical focuses.

What are the most commonly searched types of Data Analytics Engineer jobs in Colorado? The most popular types of Data Analytics Engineer jobs in Colorado are:
What are popular job titles related to Data Analytics Engineer jobs in Colorado? For Data Analytics Engineer jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Data Analytics Engineer jobs in Colorado look for? The top searched job categories for Data Analytics Engineer jobs in Colorado are:
What cities in Colorado are hiring for Data Analytics Engineer jobs? Cities in Colorado with the most Data Analytics Engineer job openings:
Data Analytics Engineer jobs near you
Infographic showing various Data Analytics Engineer job openings in Colorado as of July 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $136,399 per year, or $65.6 per hour.

AI Data Analytics Engineer

BillGO, Inc.

Fort Collins, CO • On-site

$113K - $135K/yr

Full-time

Posted 21 days ago

Job description

BillGO is building the next generation of payments. Our vision is to be the payment accelerator for Small Business: an intelligent network that helps businesses get paid faster, operate leaner, and grow with confidence.

We are hiring an AI Data Analytics Engineer to design, build, and ship the data, analytics, and AI capabilities that power BillGO's AI/Data Platform, from trusted data models and self-serve analytics that speed up internal decision-making to AI-native features that turn data into value for Small Businesses. This is a hands-on role that blends analytics engineering with applied AI at the center of BillGO's AI-native strategy.

You will sit within BillGO's Data Platform & Intelligence organization, partnering closely with Application Engineering, Platform Engineering, and Product to move data and AI capabilities from idea to production. Your work turns BillGO's AI Three-Level Framework, Internal Efficiency, Revenue Acceleration, and Customer Value, into shipped, reliable analytics and AI software.

WHAT YOU WILL OWN:

  • The design, build, and delivery of data models, analytics pipelines, and AI/ML features embedded in BillGO's payments products and internal tools.
  • Warehouse and semantic-layer modeling, metrics definitions, and self-serve analytics that make trusted data accessible across the business.
  • Integration of large language models, embeddings, and retrieval-augmented generation (RAG) systems that turn analytics data into intelligent experiences.
  • Data pipelines, evaluation frameworks, and monitoring that keep analytics and AI features accurate, safe, and observable in production.
  • Prompt engineering, model selection, and build-versus-buy tradeoffs balancing quality, latency, and cost.
  • Responsible and secure use of data and AI appropriate for a regulated payments environment.
  • Partnership with Application Engineering, Platform Engineering, and Data Platform & Intelligence to embed analytics and AI into existing services and APIs.

WHAT WE ARE LOOKING FOR:

  • 3-5 years of experience building and shipping production software or data products, with meaningful experience in analytics engineering and AI/ML feature development.
  • Proficiency in Python and SQL, and experience with modern data and AI/ML tooling: data warehouses (e.g., Snowflake, BigQuery, Redshift), transformation frameworks (e.g., dbt), LLM APIs, vector databases, embeddings, and orchestration frameworks such as LangChain, LangGraph, or similar.
  • Solid engineering fundamentals: data modeling, APIs, cloud platforms, CI/CD, testing, and observability.
  • Understanding of BI and analytics tooling, semantic layers, prompt engineering, RAG, model evaluation, and guardrail or safety practices.
  • Experience with data pipelines and both structured and unstructured data.
  • Fintech, payments, or regulated-industry experience is a plus but not required.
  • Strong collaboration and communication skills; comfortable working cross-functionally with product, platform, and data teams.

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