BI Data Engineer

Location: Brussels, Belgium

Company: Tata Consultancy Services (TCS) Belgium

Employment Type: Full-time

Nature of the tasks

• Create and maintain Enterprise Data Warehouses (EDW) and complex Business Intelligence Solutions (Data Lakes / Data Lakehouses).
• Design modern Business Intelligence (BI) architectures (e.g., data lakehouse) for cloud, on premises, and hybrid environments, ensuring compliance with security and data residency requirements.
• Gather and analyse business requirements specifically for data pipelines, storage, and reporting solutions.
• Design and implement modern Extract, Transform, Load / Extract, Load, Transform (ETL/ELT) processes and orchestration workflows, incorporating data contracts and schema evolution management.
• Implement and manage data virtualization layers to provide unified access to heterogeneous data sources while optimizing performance and security.
• Design and implement data models (e.g., dimensional modeling) to support reporting and analytics, ensuring alignment with business requirements.
• Design and build reporting and analytics applications, creating dashboards, KPIs, and self-service BI tools that deliver actionable insights to business stakeholders.
• Design and optimize the physical database schema, including indexing, partitioning, and storage strategies, to balance performance, cost, and maintainability.
• Conduct ongoing database performance analysis, tuning queries, optimizing resource utilization, and ensuring SLAs for latency and throughput are met.
• Design and implement data quality frameworks and health monitoring processes that include data observability (lineage, freshness) and defined Service Level Agreements / Service Level Objectives (SLAs/SLOs).
• Define and execute test programs for BI solutions, including data validation, regression tests, contract tests, and automated pipelines integrated into CI/CD.
• Produce and maintain technical documentation (data dictionaries, lineage, design specifications, runbooks) to support transparency, governance, and audit readiness.
• Perform deployment and configuration of BI systems and platforms, applying infrastructure-as-code, version control, and environment management practices.
• Govern the BI semantic layer (KPIs, metrics catalog, naming conventions, row-level security) to ensure consistency and trusted insights.
• Manage access controls and data security within BI platforms in alignment with GDPR and internal data protection policies.
• Optimize cost and performance of BI platforms and warehouses (partitioning, caching, clustering, query optimization, resource scaling).
• Define and enforce data lineage and stewardship practices, ensuring transparency, auditability, and compliance.
• Enable self-service BI capabilities for business users through curated datasets, certified reports, and training/support.

Specific expertise and technologies

• Knowledge of enterprise data warehouse design and architecture, including dimensional modelling and star/snowflake schema design.
• Knowledge of data lake and lakehouse design patterns (e.g., Delta Lake, Apache Iceberg) to support large-scale analytics.
• Excellent knowledge of relational database systems applied to data warehouse.
• Knowledge of non-relational databases (e.g., MongoDB, Cassandra, Hadoop HBase) for handling unstructured and semi-structured data.
• Excellent knowledge of SQL.
• Knowledge of BI reporting and analytics tools (e.g., Power BI, Tableau, Qlik) for enterprise dashboards and self-service analytics.
• Knowledge of ETL and ELT tools (e.g., Informatica, Talend, dbt, Azure Data Factory) to manage enterprise data pipelines.
• Knowledge of modelling tools (e.g., ERwin, SAP PowerDesigner, ArchiMate for data flows) to design logical and physical data models.
• Knowledge of OLAP technologies (e.g., SSAS, Essbase) and data mining tools (e.g., SAS Enterprise Miner, RapidMiner).
• Knowledge of near real-time data ingestion and change data capture (CDC) technologies (e.g., Kafka, Debezium, GoldenGate).
• Knowledge of cloud BI architectures (e.g., Azure Synapse, Snowflake, AWS Redshift) to enable scalable analytics.
• Knowledge of data governance and cataloguing practices (e.g., Collibra, Alation, Azure Purview) to ensure trusted data assets.
• Knowledge of data observability practices (e.g., freshness, lineage, anomaly detection) to monitor and assure pipeline quality.

Minimum level of expertise

• Normal

Certification and/or Standards

Optional: One of the following or an equivalent certification:

• Microsoft Data Analyst Associate
• Microsoft Azure Data Engineer Associate
• Amazon Certified Data Analytics Specialty

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