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Senior Data Analyst - Consulting

Remote, USA Full-time Posted 2026-06-26

Tiger Analytics is looking for experienced Lead Data Analyst to join our fast-growing advanced analytics consulting firm. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

We are looking for a Lead Data Analyst with strong experience in Python, SQL, ETL processes, and cloud technologies. The ideal candidate will have hands-on experience working with SQL, data analysis, and consulting, and performing data analysis to support business insights and decision-making.

This role requires someone who is comfortable working with large datasets, transforming data, and collaborating with cross-functional teams in a fast-paced environment.

Key Responsibilities

  • Partner with business stakeholders to understand and translate business needs into solution requirements.
  • Write efficient SQL queries to extract, manipulate, and analyze data from multiple sources.
  • Use Python for data processing, automation, and analytics.
  • Work with AWS /GCP/ Azure cloud services to manage and analyze data in cloud environments.
  • Perform data validation, quality checks, and troubleshooting for data pipelines.
  • Build reports, dashboards, and insights to support business decisions.
  • Facilitate large stakeholder groups and manage the complete requirements lifecycle.
  • Conduct structured analysis using both quantitative and qualitative techniques.
  • Profile data manually and document transformation logic and rules.
  • Produce clear and concise Source to Target Mapping Documents with detailed Data Mapping Requirements.
  • Collaborate effectively within an Agile environment.
  • Collaborate with data engineers, analysts, and business stakeholders to understand requirements.
  • Ensure data accuracy, consistency, and governance across systems.
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