Sql data analyst • arvada co
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The average salary range is between $ 77,499 and $ 126,740 year , with the average salary hovering around $ 97,399 year .
hackajob is collaborating with Bet365 to connect them with exceptional professionals for this role.
As a Data Analyst, you will turn complex data into clear insight that supports better decisions across our business
Within our AI and Automation department, the Data Team transforms large and complex datasets into practical reporting, clear insight and visual stories that support both operational and strategic decisions.
This role focuses on building dashboards, responding to analytical requests and turning business questions into structured analysis. Working with teams across the business, the role combines data handling, visualisation and storytelling to help make complex information easier to understand and act on.
The listed salary for this position is $90,000-$120,000 annually.
Preferred Skills, Qualifications and Experience
• Strong hands-on experience with SQL and Google BigQuery
• Experience building dashboards in Power BI and Looker
• Good understanding of data lakes, data warehouses and core data concepts
• Ability to turn complex data into clear insight for business stakeholders
• Confidence working with large datasets and spotting patterns or trends
• Experience supporting self-service analytics and improving access to data
• Skilled at creating visualisations that make information easier to interpret
• Experience working across data, engineering or business teams
• Strong problem-solving skills with a practical, detail-focused approach
• Interest in using AI to support and improve analytical delivery
Main Responsibilities
• Build clear dashboards and reports that support decision-making across the business
• Turn business questions into structured analysis and useful insight
• Work with large datasets to identify patterns, trends and opportunities
• Create visualisations and summaries that make data easier to understand
• Automate data tasks where possible to reduce manual effort
• Partner with colleagues across teams to deliver analysis effectively
• Support the improvement of reporting, processes and analytical methods
• Maintain accurate, secure and high-quality outputs throughout delivery
• Contribute to self-service analytics by making data more accessible
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