Data Analyst

Vates is a French software company specializing in virtualization, offering a credible alternative to proprietary solutions on the market. Our VATES VMS solution enables companies to manage their IT infrastructures in a simple, efficient, and controlled way.

We are looking for a Data Analyst to help us turn this data into a reliable, understandable and usable foundation for the company.

You will work at the intersection of business intelligence, data modeling and data engineering. Your role will be to transform data coming from our operational systems into well-structured, tested and documented datasets that can be confidently used for reporting, analysis and operational processes.

Your Responsibilities

Data Warehouse & Modeling

• Build and maintain data models in our Data Warehouse.
• Transform raw data from operational systems into clean, consistent and reusable datasets.
• Design analytical models and data marts supporting Sales, Finance, Marketing, Customer Success and other business functions.
• Improve existing models as business processes and requirements evolve.
• Ensure that models remain understandable, maintainable and appropriately structured as the Data Warehouse grows.

Data Quality

• Define and implement automated data quality checks.
• Identify missing, inconsistent, duplicated or anomalous data.
• Investigate discrepancies between systems and reports.
• Trace data issues back to their source and help address their root causes.
• Implement reconciliation mechanisms for critical business data.
• Make data quality measurable and visible rather than relying on manual validation.

Business Metrics & Reporting

• Work with business stakeholders to understand their reporting and analytical needs.
• Translate business concepts into precise and reproducible data definitions.
• Define and maintain shared metrics and KPIs.
• Build and maintain dashboards and reports.
• Ensure that the same business concept is calculated consistently across reports and teams.
• Help users understand the meaning, scope and limitations of the data they use.

Typical topics may include revenue, ARR, MRR, churn, sales pipeline, customers, partners, subscriptions, contracts, billing and product usage.

Data Integration

• Work with data coming from multiple internal and external applications.
• Contribute to the design of reliable ingestion and transformation pipelines.
• Understand how data flows between operational systems, the Data Warehouse and downstream consumers.
• Work with application and integration developers when changes to source systems or data flows are required.
• Participate in defining appropriate interfaces and data contracts between systems.

Documentation & Data Governance

• Document datasets, transformations, metrics and important business definitions.
• Maintain visibility over data lineage and dependencies.
• Contribute to naming conventions, modeling standards and data engineering practices.
• Help identify authoritative sources for key business information.
• Participate in improving the consistency of shared entities such as customers, organizations, products and subscriptions across systems.

Required Skills

• Mastering SQL to query, extract, filter, join and transform data from relational databases.
• Analyzing and manipulating data using Excel, Google Sheets, Python or R.
• Using Python and libraries such as Pandas, NumPy and Matplotlib for data analysis and visualization.
• Cleaning and preparing data by identifying and correcting missing, duplicated, inconsistent or inaccurate data.
• Performing statistical analyses to identify trends, correlations, patterns and anomalies.
• Building dashboards and reports using Business Intelligence tools such as Power BI, Tableau or Looker.
• Creating data visualizations to present complex information in a clear and accessible way.
• Defining and monitoring KPIs to measure business performance and support decision-making.
• Understanding relational databases and concepts such as tables, relationships, primary keys, foreign keys and joins.
• Collecting, combining and transforming data from multiple internal and external sources.
• Automating data analysis and reporting processes using SQL, Python or dedicated automation tools.
• Ensuring data quality and consistency throughout the analysis process.
• Interpreting analytical results and translating data into actionable business insights.
• Documenting data sources, queries, methodologies and analytical processes to ensure transparency and reproducibility.
• Managing ETL/ELT processes to extract, transform and load data from different sources.