Data Analyst Senior

Role Overview

Senior Data Analyst translating raw data into actionable stories that support business and risk management. You will acquire novel data sources, apply rigorous statistical methods, and communicate insights via dashboards, reports, and narrative visualizations, while supporting the organization’s data governance initiatives.

Key Responsibilities

• Data Acquisition & Enrichment

  • Identify and acquire internal/external data sources.
  • Extract, cleanse, and preprocess data from databases, APIs, and scraped sources while maintaining quality standards.
  • Work with structured data; examine multiple disparate sources to generate new insights.
  • Design and implement custom algorithms/flows/layouts for complex, enterprise-scale datasets.

• Analysis & Insight Generation

  • Apply statistical and mathematical methods to uncover trends, patterns, and risk indicators.
  • Translate findings into business-focused narratives, visualizations, and interactive dashboards (Power BI Report Server).
  • Define data requirements/specifications to provide actionable recommendations.
  • Collaborate with systems analysts, engineers, and programmers to validate source data and findings.
  • Identify hidden patterns/relationships across datasets.

• Reporting & KPI Management

  • Design, build, and maintain KPI dashboards for real-time stakeholder visibility.
  • Produce regular analytical reports and ad hoc analyses to support decision making.
  • Present insights in creative formats and communicate to both technical and non-technical audiences.

• Data Governance & Quality

  • Participate in the Data Governance Working Group.
  • Perform QA on scraping and query processes; triage and resolve data issues.

Basic/Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Business Analytics, or related field (Master’s preferred).
• Experience designing solutions considering networking concepts, network protocols, network security methodologies, cyber-threats, vulnerabilities, and cybersecurity principles.
• Knowledge of data administration, data-standardization policies, and data standards.
• Understanding of data mining, data warehousing, machine learning, and AI principles.
• Demonstrated understanding of network access and Identity & Access Management (e.g., PKI).
• Proficiency in programming logic and query languages such as SQL and object-oriented design.
• Familiarity with secure coding techniques.

Nice-to-Haves (inferred from responsibilities)

• Ability to formulate functional and technical requirements from business problems.
• Program management of key tasks/resources to achieve cost and schedule goals.