Data Analyst

Location: Brussels, Belgium

Company: Tata Consultancy Services (TCS) Belgium

Employment Type: Full-time

Nature of the Tasks

• Collect and clean data from various sources.
• Analyse datasets to identify trends and patterns.
• Create interactive dashboards and reports that utilize data storytelling to effectively present findings, with defined refresh cadences and Service Level Agreements (SLAs) for key deliverables.
• Collaborate with stakeholders to understand data needs.
• Develop and maintain data dashboards and reporting tools that provide intuitive access to key metrics and insights, supported by clear documentation and update schedules.
• Perform statistical analysis to support decision making.
• Ensure data accuracy and consistency through the implementation of data quality checks, validation rules, and regular reconciliation processes with source systems.
• Conduct exploratory data analysis to uncover insights.
• Document methodologies and analysis processes.
• Recommend improvements based on data insights.
• Partner with Data Engineers on data modelling (e.g., star/snowflake schemas) and contribute to a centralized definitions and metrics catalogue.
• Present data-driven insights and create actionable, decision-oriented recommendations tailored for non-technical audiences and stakeholders.
• Write complex Structured Query Language (SQL) queries for data extraction and transformation, and optimize them for performance and efficiency.

Specific Expertise and Technologies

• Knowledge of data analysis and manipulation using SQL, Python, or R to extract, transform, and analyse structured and unstructured datasets.
• Experience with data visualization and reporting tools (e.g., Power BI, Qlik, Microsoft Fabric) to design interactive dashboards and reports.
• Knowledge of statistical analysis methods and tools (e.g., SPSS, R, Python libraries) to support evidence-based insights.
• Familiarity with spreadsheet tools, especially Microsoft Excel.
• Knowledge of data warehousing concepts and platforms (e.g., star/snowflake schemas, Microsoft Fabric, Snowflake, BigQuery) to support scalable reporting.
• Knowledge of data preparation and transformation tools (e.g., Alteryx, Power Query, dbt) to ensure clean and reliable datasets.
• Ability to work with large datasets and databases.
• Awareness of basic machine learning algorithms (e.g., regression, clustering, classification) to support advanced analytics in collaboration with data scientists.
• Knowledge of ETL processes and orchestration tools (e.g., SSIS, Informatica, Talend, Azure Data Factory) to support data pipelines.
• Knowledge of cloud-based data services (e.g., Microsoft Fabric, Azure Synapse) to enable scalable and modern analytics.
• Knowledge of data quality practices (e.g., validation rules, reconciliation, lineage tracking) to ensure accuracy and consistency of insights.
• Knowledge of metadata and cataloguing tools (e.g., Azure Purview, Collibra) to support data governance and discoverability.

Minimum Level of Expertise

• Junior for analysts supporting a senior team.
• Normal for standalone Data Analysts accountable for business domains.

Certification and/or Standards

Optional for 'Junior' level:

• A certification directly related to the requested profile.

Optional for 'Normal' level and above:

• Microsoft Power BI Data Analyst Associate
• Microsoft Azure Data Engineer Associate
• Fabric Analytics Engineer Associate
• Fabric Data Engineer Associate
• Or an equivalent certification

Skills

• Ability to analyse datasets, detect trends, and deliver evidence-based insights.
• Skill in presenting findings with visuals and storytelling that support decisions.
• Commitment to maintaining accuracy, consistency, and integrity in reporting.
• Ability to resolve data issues and propose actionable improvements.
• Competence in managing tasks and delivering insights on time.
• Drive to explore new methods, tools, and data sources proactively.
• Ability to interpret analytical findings to tell a compelling story that clearly prescribes business actions and drives strategic decision-making.
• Flexibility to adopt new data platforms, tools, and approaches.
• Ability to frame insights into narratives that support decisions.
• Ability to participa