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Data Analyst

Posted 23 days agoViewed

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💎 Seniority level: Middle, Minimum of 4 years experience

📍 Location: United Kingdom

💸 Salary: 42000.0 - 50000.0 GBP per year

🔍 Industry: SaaS

🏢 Company: Vable👥 11-50💰 $1,000,000 Seed over 8 years agoInternetSaaSInformation TechnologySoftware

🗣️ Languages: English

⏳ Experience: Minimum of 4 years experience

🪄 Skills: PythonSQLETLGoogle AnalyticsData visualization

Requirements:
  • A Bachelor’s Degree in Data Analytics, Statistics, Computer Science, or a related field.
  • Minimum of 4 years experience in a data analysis or similar role, preferably within a fast-paced SaaS or technology environment.
  • Proficiency in data analysis tools and languages, such as Python, R or similar for advanced data manipulation and analysis.
  • Understanding of the data pipeline, with familiarity with ETL processes.
  • Familiarity with statistical analysis techniques e.g., regression, hypothesis testing.
  • Experience working with large datasets and deriving actionable insights.
  • Proficient in Lucene query syntax for querying and analyzing data in OpenSearch.
  • Strong SQL skills for working with AWS Quicksight to create reports and visualizations.
  • Advanced Excel capabilities for quick data manipulation and supplementary analysis.
  • Experience working with Google Analytics for user behavior tracking and performance metrics.
  • Familiarity with Hotjar for heatmaps and user interaction insights.
  • Ability to define and monitor product KPIs and OKRs.
  • Effective communication and interpersonal skills to work collaboratively and present findings.
Responsibilities:
  • Collect, clean, and analyze data from various sources to identify trends and insights.
  • Collaborate with departments, including Sales, Marketing, Product, Engineering, and Client Success, to understand their data needs and provide tailored insights.
  • Provide statistical analysis to validate experiment results and recommend next steps.
  • Ensure data integrity by regularly auditing and maintaining data cleanliness within systems like CRMs and marketing platforms.
  • Partner with the Client Success team to identify and track key metrics driving client satisfaction and retention.
  • Develop models to predict churn and identify at-risk users.
  • Provide training and tools for teams to access and utilize data effectively.
  • Build and maintain dashboards for real-time monitoring of these metrics.
  • Interpret data and generate actionable recommendations for the team.
  • Analyze client behavior to understand engagement patterns, activation rates, and drop-off points.
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