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Manager Data Analytics Engineer Jobs in Colorado

BillGO, Inc.

AI Data Analytics Engineer

Fort Collins, CO

$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 ...

US Bank

Infrastructure Data Analytics Engineer

Denver, CO · On-site

$117K - $141K/yr

The Infrastructure Data Analytics Engineer is responsible for acquiring, transforming, integrating ... Infrastructure monitoring platforms * CMDB and asset management systems * Cloud platforms (Azure ...

Summit Utilities Inc

Analytics Engineer II

Denver, CO · On-site

We have an exciting hybrid opportunity for an Analytics Engineer II (SGL17) based in Denver, CO ... Familiarity with data governance and metadata management practices * Ability to gather requirements ...

Pwc

Data Engineer - Manager

Denver, CO · On-site

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and ...

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Manager Data Analytics Engineer information

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

AspectManager Data Analytics EngineerData Analytics Engineer
Required CredentialsBachelor's or Master's in Data Science, Analytics, or related field; often leadership experienceBachelor's or Master's in Data Science, Analytics, or related field
Work EnvironmentLeads teams, manages projects, collaborates with stakeholdersDevelops data models, analyzes data, implements solutions
Employer & Industry UsageUsed in tech, finance, healthcare, and large enterprisesCommon in similar industries, often within data teams

The main difference is that a Manager Data Analytics Engineer oversees teams and projects, focusing on leadership and strategic planning, while a Data Analytics Engineer primarily develops and implements data solutions. Both roles require strong technical skills, but the manager role adds a layer of team management and stakeholder communication.

How does a manager data analytics engineer typically balance technical project work with team leadership responsibilities?

As a Manager Data Analytics Engineer, you are expected to split your time between overseeing complex analytics engineering tasks and guiding your team’s development. This involves setting project priorities, conducting code reviews, and ensuring data solutions align with business goals, while also mentoring team members and facilitating collaboration with stakeholders like data scientists and business analysts. Successful managers often establish clear communication channels and delegate tasks effectively, so they can stay hands-on with key projects while supporting the professional growth of their team.

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

To thrive as a Manager Data Analytics Engineer, you need a strong background in data engineering, analytics, and leadership, typically with a degree in computer science or a related field. Familiarity with tools like SQL, Python, data warehousing platforms (e.g., Snowflake, Redshift), and certifications in cloud technologies or data management are common requirements. Excellent communication, problem-solving, and team management skills set top performers apart in this role. These competencies are essential for driving data strategy, ensuring data quality, and leading analytics teams to deliver actionable business insights.

What is a manager data analytics engineer?

A Manager Data Analytics Engineer is a professional who leads a team of data analytics engineers responsible for designing, building, and maintaining data systems and analytics solutions. They oversee data pipeline development, ensure data quality, and collaborate with stakeholders to translate business requirements into technical solutions. In addition to technical expertise, they manage project timelines, mentor team members, and help drive data-driven decision-making across the organization.

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 Manager Data Analytics Engineer jobs in Colorado?

For Manager Data Analytics Engineer jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Manager Data Analytics Engineer jobs in Colorado look for?

The top searched job categories for Manager Data Analytics Engineer jobs in Colorado are:

What cities in Colorado are hiring for Manager Data Analytics Engineer jobs?

Cities in Colorado with the most Manager Data Analytics Engineer job openings:

Manager Data Analytics Engineer jobs near you
Infographic showing various Manager Data Analytics Engineer job openings in Colorado as of July 2026, with employment types broken down into 87% Full Time, 12% Part Time, and 1% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution.

AI Data Analytics Engineer

BillGO, Inc.

Fort Collins, CO

$113K - $135K/yr

Full-time

Re-posted yesterday

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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