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Analytic Engineer Jobs in Colorado (NOW HIRING)

The Analytics Enablement Manager is responsible for leading analytics engineers within Data Services while shaping and driving a cohesive, enterprisewide analytics strategy. This leader brings a ...

The Analytics Enablement Manager is responsible for leading analytics engineers within Data Services while shaping and driving a cohesive, enterprise-wide analytics strategy. This leader brings a ...

Senior Analytics Engineer

Denver, CO · On-site +1

$135K - $180K/yr

About the Role Orchard is looking for a Senior Analytics Engineer to help rebuild how the business gets answers from its data. We're moving from a sprawling Looker footprint to a set of certified ...

Site Reliability Engineer

Denver, CO

$58.75 - $78/hr

Analytic Partners is a global leader in commercial measurement and optimization, turning data into ... Own the Internal Developer Platform (IDP) as a product, treating engineering teams as customers and ...

Senior Analytics Engineer

Denver, CO · On-site

$135K - $180K/yr

About the Role Orchard is looking for a Senior Analytics Engineer to help rebuild how the business gets answers from its data. We're moving from a sprawling Looker footprint to a set of certified ...

Engineer V

Englewood, CO · On-site

$105K - $189K/yr

We have an exciting opportunity for a Spacecraft Structural Dynamics Analysis Engineer to join our Mechanical Engineering Team working with satellite development and orbital activities. DUTIES AND ...

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Analytic Engineer information

See Colorado salary details

$41K

$107K

$144.6K

How much do analytic engineer jobs pay per year?

As of Jun 12, 2026, the average yearly pay for analytic engineer in Colorado is $106,994.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,300.00 and $122,500.00 per year, depending on experience, location, and employer.

What is the best synonym for analytic?

For an Analytic Engineer, a good synonym for analytic is 'analytical,' which emphasizes skills in data analysis, critical thinking, and problem-solving. Other related terms include 'logical,' 'methodical,' or 'data-driven,' reflecting the focus on interpreting complex data sets and deriving insights using tools like SQL, Python, or Tableau.

What is the difference between Analytic Engineer vs Data Engineer?

AspectAnalytic EngineerData Engineer
CredentialsTypically requires a degree in data science, statistics, or related fields; often certifications in SQL, Python, or cloud platformsRequires a degree in computer science, software engineering, or related fields; certifications in cloud services, SQL, and data pipeline tools
Work EnvironmentFocuses on analyzing data, building data models, and creating dashboards; collaborates with data scientists and business teamsBuilds and maintains data pipelines, databases, and infrastructure; works closely with data engineers and software developers
Industry UsageCommonly found in analytics teams, business intelligence, and data-driven decision-making rolesPrimarily in data infrastructure, big data projects, and data platform development

In summary, Analytic Engineers focus on transforming data into insights through analysis and modeling, while Data Engineers build the infrastructure to support data collection and storage. Both roles are essential in data teams but serve different functions within the data ecosystem.

What do you mean by analytic?

In the context of an Analytic Engineer role, an analytic refers to the process of examining data to uncover insights, patterns, and trends that support business decision-making. It involves using statistical methods, data visualization tools, and programming skills to interpret large datasets and generate actionable reports.

What does it mean if someone is analytic?

An analytic in the context of an Analytic Engineer refers to a person who uses data analysis skills to interpret complex data sets, identify patterns, and generate insights. They often work with tools like SQL, Python, or data visualization software to support decision-making and improve business processes.

Is analytic a good ability in Pokémon?

In the context of an Analytic Engineer, analytical skills are highly valuable for interpreting data, identifying patterns, and making data-driven decisions. While 'analytic' as a skill is relevant in many fields, in Pokémon, it refers to a game mechanic that increases damage when the opponent is hit after being attacked, which is unrelated to professional skills. Therefore, analytical ability is beneficial for data roles but not applicable to Pokémon gameplay mechanics.
What cities in Colorado are hiring for Analytic Engineer jobs? Cities in Colorado with the most Analytic Engineer job openings:
Infographic showing various Analytic Engineer job openings in Colorado as of June 2026, with employment types broken down into 1% As Needed, 78% Full Time, 16% Part Time, 3% Contract, and 2% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $106,994 per year, or $51.4 per hour.
Manager Analytics Enablement

Manager Analytics Enablement

Sonepar USA

Broomfield, CO • On-site

Full-time

Posted 12 days ago


Sonepar USA rating

7.4

Company rating: 7.4 out of 10

Based on 31 frontline employees who took The Breakroom Quiz

158th of 338 rated retail wholesalers


Job description

Job Summary:
Sonepar USA is a company that supports its brands through a shared services model, fostering an inclusive culture. The Analytics Enablement Manager will lead analytics engineers and drive a cohesive analytics strategy to enhance analytical quality and consistency across the organization.
Responsibilities:
• Define and drive an enterprise analytics enablement strategy that improves analytical quality, consistency, and trust across Operating Companies and Sonepar Management Group.
• Establish clear expectations and best practices for analytical problem framing, metric definition, and solution design - independent of specific tools or platforms.
• Act as a strategic advisor to business and analytics leaders on when and how different analytics approaches and productivity tools should be used, based on business need and maturity.
• Partner closely with Data Governance to ensure analytics practices align to a unified governance model, including standards for metrics, documentation, reuse, and ownership.
• Manage and develop analytics engineers within the centralized Data Services team, setting direction for how analytical solutions are designed, reviewed, and enabled across the organization.
• Promote modern analytics engineering practices, including modular design, reuse, documentation, and performance‑aware modeling.
• Ensure analytics engineers are focused on enablement and leverage, not long‑term ownership of bespoke reporting.
• With the Data Product Manager, support alignment between central Data Services and distributed Operating Company / Sonepar Management Group analysts, reducing fragmentation and duplicated effort.
• Support analyst communities by identifying common analytical patterns, shared needs, and opportunities for standardization or reuse.
• Influence without direct authority - driving alignment through clarity, shared frameworks, and strong analytical reasoning.
• Help shift the organization from reactive, ad‑hoc reporting toward prioritized, outcome‑driven analytics work.
• Provide strategic guidance on analytical usage of Power BI and the broader Microsoft analytics ecosystem.
• Bring informed perspective on modern analytics stacks from prior experience (e.g., SQL‑based modeling, semantic layers, cloud data platforms), without requiring deep specialization in any single tool.
• Understand and advise on analytical use cases related to Sonepar’s in‑house productivity applications, while not directly managing or delivering those solutions.
Qualifications:
Required:
• Strong analytical problem‑solving orientation, with the ability to translate ambiguous business questions into clear, structured analytical approaches and articulate tradeoffs and assumptions.
• 7+ years of experience in analytics, analytics engineering, business intelligence, or a closely related field, including experience operating in complex, matrixed organizations.
• Demonstrated experience leading or influencing analytics engineers, analysts, or analytics communities through standards, coaching, and enablement.
• Proven exposure to modern analytics platforms and practices across multiple environments (e.g., cloud data platforms, semantic modeling, analytics engineering, BI tools), with the ability to apply best practices pragmatically.
• Strong understanding of analytics governance concepts, including metric consistency, reuse, documentation, data product ownership, and lifecycle management.
• Ability to operate effectively across centralized and federated analytics models, balancing autonomy with enterprise consistency.
• Excellent communication skills, with the ability to engage credibly with technical teams, business leaders, and senior executives.
• Comfort influencing without authority and navigating organizational ambiguity while maintaining focus on outcomes.
• Understanding of modern analytics architecture concepts, including data transformation patterns, semantic/metrics layers, and analytical model design.
• Familiarity with analytics development best practices such as version control, peer review, and environment promotion, sufficient to guide standards and reviews.
Preferred:
• Experience working across multiple companies or industries, with exposure to varying levels of analytics maturity.
• Background in analytics engineering, data analytics, or business intelligence with strong grounding in technical best practices (not just report development).
• Experience supporting or enabling distributed analyst communities.
• Familiarity with Power BI and the Microsoft Fabric ecosystem, paired with openness and credibility across other modern analytics tools and approaches.
• Experience working with relational databases and analytical data stores, including writing and reviewing SQL and understanding performance considerations.
• Working knowledge of Python for analytics, automation, and data transformation use cases.
• Foundational understanding of data engineering concepts, including data movement, transformations, and dependency management (hands‑on expertise not required).
Company:
Sonepar USA is a wholesale company providing electrical, industrial, and safety products. Founded in 1998, the company is headquartered in Charleston, USA, with a team of 10001+ employees. The company is currently Late Stage.

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