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Entry Level Data Analyst Fintech Jobs in Colorado

The Opportunity: We're looking for an entry level Data Engineer to join the Ookla Data Engineering ... Support and collaborate with data scientists, analysts, and product managers Job Requirements:

The Opportunity: We're looking for an entry level Data Engineer to join the Ookla Data Engineering ... Support and collaborate with data scientists, analysts, and product managers Job Requirements:

Data Engineer

Denver, CO · On-site +1

$85K - $125K/yr

Description Position at Ookla The Opportunity: We're looking for an entry level Data Engineer to ... Support and collaborate with data scientists, analysts, and product managers Job Requirements:

Data Engineer

Denver, CO · On-site +1

$85K - $125K/yr

Description The Opportunity: We're looking for an entry level Data Engineer to join the Ookla Data ... Support and collaborate with data scientists, analysts, and product managers Job Requirements:

The Opportunity: We're looking for an entry level Data Engineer to join the Ookla Data Engineering ... Support and collaborate with data scientists, analysts, and product managers Job Requirements:

Data Engineer

Denver, CO · On-site +1

$85K - $125K/yr

Description Position at Ookla The Opportunity: We're looking for an entry level Data Engineer to ... Support and collaborate with data scientists, analysts, and product managers Job Requirements:

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Entry Level Data Analyst Fintech information

What is the difference between Entry Level Data Analyst Fintech vs Entry Level Data Scientist Fintech?

AspectEntry Level Data Analyst FintechEntry Level Data Scientist Fintech
Required CredentialsBachelor's in Data, Finance, or related field; basic SQL and Excel skillsBachelor's in Data Science, Statistics, or related; programming skills in Python/R; some certifications
Work EnvironmentData analysis teams within fintech companies, focusing on reporting and dashboardsData science teams, working on predictive models and advanced analytics
Employer & Industry UsageCommon in fintech firms for operational insightsLess common at entry level, more in research and development roles

Entry Level Data Analyst Fintech roles focus on interpreting data, creating reports, and supporting decision-making with basic analytics. Entry Level Data Scientist Fintech positions involve more advanced statistical modeling and programming. While both roles require a strong foundation in data concepts, data scientists typically need additional technical skills and certifications. The analyst role is more accessible for beginners, whereas data scientist roles are more specialized and technical.

What does an entry level data analyst do in fintech?

An entry level data analyst in fintech is responsible for collecting, cleaning, and interpreting data related to financial products and services. Their work typically involves analyzing large datasets to identify trends, support business decisions, and improve financial products. They may also create dashboards or reports to visualize data findings for other teams. Additionally, entry level analysts often collaborate with engineers, product managers, and senior analysts to ensure data accuracy and relevance. This role provides a strong foundation in both data analysis and the unique challenges of the financial technology sector.

What are the key skills and qualifications needed to thrive as an entry level data analyst in fintech, and why are they important?

To thrive as an Entry Level Data Analyst in Fintech, you need strong analytical skills, a solid grasp of statistics, and a degree in a related field such as mathematics, finance, or computer science. Familiarity with data analysis tools like SQL, Python, and Excel, as well as financial data platforms, is typically expected. Attention to detail, problem-solving abilities, and effective communication are vital soft skills for interpreting data and sharing insights with stakeholders. These skills are important for accurately analyzing complex financial data, driving data-driven decisions, and supporting the company's strategic goals.

What are some typical projects or tasks that an entry level data analyst in fintech might work on?

As an entry level data analyst in fintech, you’ll often assist with tasks such as cleaning and preparing large financial datasets, creating basic reports and dashboards, and supporting more senior analysts with ad-hoc data requests. You may also help monitor transaction trends for fraud detection, contribute to customer segmentation projects, and automate routine data processes. Collaboration with product, engineering, and compliance teams is common, and you’ll gain valuable exposure to the fast-paced, data-driven decision-making environment typical of fintech companies.

What are the most commonly searched types of Data Analyst Fintech jobs in Colorado?

The most popular types of Data Analyst Fintech jobs in Colorado are:

What are popular job titles related to Entry Level Data Analyst Fintech jobs in Colorado?

For Entry Level Data Analyst Fintech jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Entry Level Data Analyst Fintech jobs in Colorado look for?

The top searched job categories for Entry Level Data Analyst Fintech jobs in Colorado are:

What cities in Colorado are hiring for Entry Level Data Analyst Fintech jobs?

Cities in Colorado with the most Entry Level Data Analyst Fintech job openings:

Infographic showing various Entry Level Data Analyst Fintech job openings in Colorado as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Full-time

Re-posted 28 days ago


Job description

About the Team

Launched in 2021, Fanatics Betting and Gaming is the online and retail sports betting subsidiary of Fanatics, a global digital sports platform. The Fanatics Sportsbook is available to 95% of the addressable online sports bettor market in the U.S. Fanatics Casino is currently available online in Michigan, New Jersey, Pennsylvania and West Virginia. Fanatics Betting and Gaming operates twenty-two retail sports betting locations, including the only sportsbook inside an NFL stadium at Northwest Stadium. Fanatics Betting and Gaming is headquartered in New York with offices in Denver, Leeds and Dublin.

Overview

As an Operations Data Analyst at Fanatics Betting & Gaming, you are on the front lines of how the Operations Department understands its own performance. You sit within the Analytics & AI Enablement team and own the data pipelines, reporting, and analytical work that CX, Fraud, Payments, WFM, and VIP leaders rely on to make decisions every day.

This is not a passive reporting role. You are expected to take ownership across multiple operational areas, building and maintaining the data layer, surfacing insights, and flagging risks before they become crises. You work closely within the Strategy & Analytics team to ensure our data is accurate, scalable, and directly connected to Ops top-line goals.

We are actively building toward an AI-first way of operating, and this role is part of that. The ideal candidate is technically sharp, relentlessly detail-oriented, and genuinely curious, both about what the data is saying and about how AI can change the way we find and act on answers. You are comfortable moving fast, owning ambiguous problems, and holding yourself to a high bar without being told to.

Responsibilities

Analytical Work & Insights

  • Respond to high-priority analytical requests from Ops leaders - turning raw data into clear, actionable insights on a tight timeline.
  • Proactively surface trends, anomalies, and risks from the data without waiting to be asked.
  • Support scenario analysis and impact sizing for product initiatives, operational changes, and staffing decisions.
  • Own the development and maintenance of dashboards and reports that give operational leaders clear visibility into performance across CX, Fraud, Payments, WFM, and VIP.
  • Ensure all reporting reflects up-to-date data, clearly defined KPIs, and documented assumptions.
  • Present findings and data narratives directly to operational stakeholders - translating complexity into clear recommendations they can act on.

Data Infrastructure & Pipelines

  • Build, maintain, and improve data pipelines that feed Ops reporting and dashboards - ensuring consistent, accurate, and well-documented data flows.
  • Partner with Data Engineering on DBT development, data store buildout, and pipeline reliability.
  • Proactively identify and resolve data quality issues; escalate blockers that require cross-functional resolution.
  • Deprecate manual, one-off data pulls and replace with automated, always-on solutions.
  • Build alerting infrastructure on critical Ops metrics to catch issues early and reduce reactive firefighting.

AI Enablement & Innovation

  • Support the AI agent roadmap by contributing data, analytical rigor, and validated data foundations before an agent moves to build.
  • Track and report on AI agent performance post-deployment - measuring impact against top-line Ops goals.
  • Actively look for opportunities to apply AI to your own workflow - whether that's speeding up analysis, improving accuracy, or eliminating manual work.
  • Bring a point of view on where AI can and can't be trusted, and flag where human judgment needs to stay in the loop.
Required Qualifications
  • 2+ years of experience in an analytical role - business intelligence, data analytics, strategic operations, or a related field.
  • Strong hands-on SQL experience; ability to write, QA, and optimize complex queries independently.
  • Experience supporting AI/ML workflows, agent builds, or automation initiatives in an analytical capacity.
  • Experience building and maintaining dashboards in Sigma, Tableau, or a comparable data visualization tool.
  • Familiarity with DBT or similar data transformation frameworks.
  • High attention to detail - you catch data quality issues before they surface in leadership reporting.
  • Strong communication skills; able to translate analytical findings into plain language for operational stakeholders.
  • Comfortable operating in fast-paced, ambiguous environments with shifting priorities.
Preferred Qualifications
  • Familiarity with operational KPIs across customer support, fraud, payments, or workforce management.
  • Experience with Python or similar scripting languages for data manipulation and automation.
  • Experience in gaming, fintech, sports, or other operationally intensive, high-volume environments.
  • Bachelor's degree in Analytics, Computer Science, Statistics, Economics, or a related field.

Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE-a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process.

For information about our benefits, please visit https://benefitsatfanatics.com/