Data Analyst (Azure)

About You

You are a data-driven professional who enjoys transforming complex datasets into clear, actionable insights that drive business decisions. You combine strong analytical and statistical skills with a solid understanding of business needs, and you thrive in collaborative environments where data storytelling and impact matter. You are detail-oriented, proactive, and comfortable working with stakeholders to translate requirements into meaningful metrics and reports.

You Bring to Applaudo the Following Competencies:

• Bachelor’s Degree or higher in Computer Science, Computer Engineering, Data Analytics, or a related field.
• 4-5 years of hands-on experience in data analysis, reporting, and business intelligence.
• Strong experience with Power BI for data visualization, dashboarding, and reporting.
• Proficiency in Python for data manipulation and analysis; familiarity with Qlik is a plus.
• Advanced knowledge of SQL, including complex queries, stored procedures, and performance optimization.
• Experience with quantitative research, statistical analysis, and data modeling to generate insights.
• Familiarity with SSIS/SSRS and GitHub for version control and reporting workflows.
• Ability to translate data into compelling, business-oriented narratives and recommendations.
• Experience working with stakeholders in fast-paced, client-facing environments.
• Understanding of software development best practices and exposure to cloud-based data solutions.
• Excellent English communication skills, as you will work directly with US-based clients.

You Will Be Accountable for the Following Responsibilities:

• Gather and analyze business requirements to define KPIs, metrics, and reporting needs.
• Design, develop, and maintain dashboards and reports that provide clear and actionable insights.
• Collect and integrate data from multiple sources (databases, APIs, files, streams) into analysis-ready datasets.
• Clean, validate, and prepare data by resolving inconsistencies, errors, and missing values.
• Apply statistical techniques and analytical methods to identify trends, patterns, and opportunities.
• Collaborate closely with cross-functional teams to deliver insights that support strategic decision-making.
• Support predictive analysis and forecasting initiatives using statistical and machine learning techniques.
• Ensure data quality, governance, and security standards are consistently met.
• Communicate findings clearly to both technical and non-technical stakeholders through reports and presentations.