Brightfield - Data Operations Analyst
Requirements
• Educational Background: Bachelor's degree in a relevant field (e.g., Business, Analytics, Statistics, Mathematics, Economics) or equivalent work experience. • Educational Background: • Experience Level: 2+ years of experience in data analysis, business analysis, consulting, customer-facing analytics, or a related role; experience working directly with customers, clients, or business stakeholders; demonstrated ability to solve business problems using data. 2+ years of Dev-Ops, Internal tools development, or full stack systems engineering, with an emphasis on automation. • Communication and Presentation: Strong written and verbal communication skills; ability to explain analytical findings to non-technical audiences; confidence participating in customer meetings and presentations; demonstrated ability to listen actively, ask clarifying questions, and accurately translate customer requirements into analytical solutions. • Communication and Presentation: • Technical Proficiency: Advanced proficiency in Excel, including Pivot Tables, XLOOKUP/VLOOKUP, INDEX/MATCH, logical formulas, data validation, Python, Docker, Git, and large dataset manipulation; proficiency in SQL for data extraction and analysis; experience preparing reports, analyses, and customer-facing deliverables. • Technical Proficiency: • Analytical and Problem-Solving Skills: Strong problem-solving and critical-thinking abilities; ability to break down ambiguous business challenges into structured analytical approaches; strong attention to detail and commitment to data quality; ability to identify trends, anomalies, inconsistencies, and actionable insights within datasets. • Professional Competencies: Naturally curious and proactive; comfortable working through ambiguity and incomplete requirements; strong sense of ownership and accountability; highly organized and dependable; committed to continuous improvement; capable of managing multiple priorities while delivering accurate, high-quality work and exceptional customer experiences. • Professional Competencies:
Responsibilities
• Data Analysis and Problem Solving: Analyze workforce and business data to identify trends, patterns, and opportunities; investigate customer questions and business challenges using a structured analytical approach; translate business needs into data-driven solutions and recommendations; identify anomalies, inconsistencies, and potential issues within datasets; support the development of customer deliverables and reporting solutions. • Data Analysis and Problem Solving: • Customer Partnership and Communication: Work directly with customers to understand business objectives and reporting requirements; participate in customer meetings and presentations; present findings and recommendations in a clear, concise, and actionable manner; ask thoughtful clarifying questions to ensure requirements and expectations are fully understood; provide timely updates on deliverables and project status; build trusted relationships through responsiveness, professionalism, and follow-through. • Customer Partnership and Communication: • Data Quality and Standards: Ensure data accuracy, consistency, and completeness across datasets and deliverables; perform quality assurance reviews of analyses and reports prior to customer delivery; investigate and resolve data discrepancies; maintain organized documentation of methodologies, assumptions, and findings. • Data Quality and Standards: • Project and Portfolio Management: Manage multiple projects and customer requests simultaneously; prioritize work effectively in a fast-paced environment; collaborate with Customer Success, Product, and Data teams to support customer initiatives; adapt to changing priorities while maintaining quality and meeting deadlines. • Project and Portfolio Management: • Business Acumen and Continuous Learning: Develop an understanding of customer needs, business processes, and industry concepts to inform analytical solutions; demonstrate curiosity and a willingness to learn new business concepts, industries, tools, and analytical methodologies; seek and apply feedback; stay current with emerging trends, tools, and best practices in data analysis; share knowledge and insights to support a collaborative team environment. • Business Acumen and Continuous Learning: • Tooling and Automation: Work with the Data Analyst and Platform Engineering teams to identify areas where automation can be leveraged to reduce time to completion of regular tasks. Develop internal tools and automations using Python, and SQL. • Tooling and Automation:
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