Mid-Level Data Analyst at PureIntegration

We are seeking a Mid-Level Data Analyst to support data-driven decision-making across network reliability and subscriber experience initiatives. This role is responsible for performing structured analysis on large, complex datasets; identifying trends and contributing factors; and delivering clear, actionable insights to technical and operational stakeholders. This position is ideal for an analyst who can work independently on well-defined problems while also partnering closely with a senior lead on more complex investigations. The role emphasizes strong fundamentals in SQL, Excel, data visualization, and analytical reasoning, with a focus on answering both what happened and why it happened. The ideal candidate will be comfortable working with large-scale datasets, building repeatable analyses, and using data to support troubleshooting, operational improvement, and measurement of network changes. This position is fully remote within the U.S. and will be a W-2 hourly engagement for 6 to 9 months. The hourly range is $40/hr - $50/hr. Candidates will be paid based on their work experience and skills. Candidates are also eligible for limited benefits such as health insurance, professional development, training, our referral bonus program, and our wellness program. Responsibilities: • Perform structured analysis on large-scale datasets to identify trends, patterns, anomalies, and potential drivers of service performance outcomes. • Write and execute SQL queries in Amazon Redshift to extract, join, and analyze data from established sources. • Use Excel as a primary analytical tool, including pivot tables, formulas, reconciliations, and purpose-built aggregations. • Develop, modify, and interpret dashboards and visualizations in Tableau and MicroStrategy to support exploratory and explanatory analysis. • Support hypothesis-driven investigations by segmenting data across dimensions such as geography, equipment, network configuration, and operational history. • Assist with before-and-after analyses and comparative studies to help determine whether changes improved or degraded outcomes. • Read and debug Python code used in analytical workflows, data validation, or lightweight automation; ability to write scripts is preferred but not required. • Apply an AI-aware analytical mindset by identifying opportunities to use AI tools to accelerate analysis, improve first-pass exploration, or strengthen communication of findings. • Prepare clear, well-structured summaries of findings for technical and non-technical stakeholders through email, PowerPoint, and supporting documentation. • Partner with senior analysts, subject matter experts, and cross-functional stakeholders to refine problem statements, validate findings, and support business decisions. • Ensure analytical accuracy by validating outputs, checking aggregation logic, and reconciling results where needed. • Manage multiple assignments and deliver timely, high-quality work in a fast-paced environment.