Data Architect

ppbLocation: /b Brussels, Belgium /p pbCompany: /b Tata Consultancy Services (TCS) Belgium /p pbEmployment Type: /b Full-time /p h3Nature of the Tasks /h3 ul liDevelop and implement the organization's overarching data strategy, creating blueprints for data management that align with and enable key business objectives. /li liTranslate business requirements into technical specifications and data architecture designs, ensuring the data infrastructure supports both immediate and long-term needs. /li liCreate conceptual, logical, and physical data models (e.g., dimensional for analytics) that define data structure, relationships, and storage. /li liMaintain metadata repositories to ensure data accuracy, lineage, and integration, curating both technical and business metadata for clarity. /li liArchitect solutions to integrate data from disparate sources (ERPs, CRMs) using ETL/ELT processes and tools (e.g., Apache NiFi, Talend) into a unified framework. /li liBuild and manage streaming data pipelines (e.g., using Kafka, Spark Streaming) to support real-time analytics and decision-making. /li liDefine and enforce data governance policies, including data quality standards, lineage tracking, access controls, and a data catalog. /li liImplement security protocols (encryption, RBAC) and design architectures to ensure adherence to regulations like GDPR. /li liImplement processes for data profiling, validation, and cleansing to ensure ongoing data accuracy, consistency, and reliability. /li liEvaluate and select appropriate database systems (SQL, NoSQL), cloud platforms (AWS, Azure, GCP), and tools that meet scalability and performance needs. /li liArchitect and deploy scalable data solutions in cloud (e.g., Snowflake) or hybrid environments, optimizing for cost-efficiency. /li liMonitor, troubleshoot, and optimize data systems and pipelines for performance, scalability, and cost. /li liWork with business leaders, data engineers, and scientists to ensure the architecture meets diverse needs and bridges technical and non-technical gaps. /li liMentor data teams on best practices, standards, and tools; lead data-centric projects and strategic initiatives. /li liOversee the entire data lifecycle, from collection and storage to archiving and purging, ensuring data remains manageable and relevant. /li liStay abreast of trends in big data, AI, and cloud computing to continuously innovate and modernize the data architecture. /li /ul h3Specific Expertise and Technologies /h3 ul liKnowledge of enterprise data architecture methods and reference models (e.g., DAMA-DMBOK, Data Mesh principles, Data Fabric patterns). /li liKnowledge of data modelling approaches: 3NF, dimensional/star-schema, and Data Vault 2.0; experience with modelling languages/tools (e.g., ER, UML, ArchiMate; ERwin, SAP PowerDesigner). /li liExperience with metadata and cataloguing platforms to govern lineage and ownership (e.g., Collibra, Alation, Azure Purview, OpenLineage). /li liKnowledge of data governance and quality frameworks (e.g., ISO 8000, ISO/IEC 11179), including stewardship, data domains, and controls. /li liUnderstanding of privacy, security, and compliance requirements (e.g., GDPR, ISO/IEC 27001), including encryption, key management, RBAC/ABAC, and data residency. /li liExperience with integration patterns and pipelines: ETL/ELT, CDC, event streaming (e.g., Kafka, Debezium) and orchestration (e.g., Airflow, Azure Data Factory, Dagster). /li liKnowledge of lakehouse and warehouse architectures, table/format standards (e.g., Delta Lake, Apache Iceberg, Apache Hudi) and columnar formats (e.g., Parquet). /li liExperience with cloud data platforms such as Azure Synapse, Databricks, Microsoft Fabric, Snowflake, and Amazon Redshift. /li liKnowledge of relational and NoSQL data stores and when to apply them (e.g., PostgreSQL, Oracle, MongoDB, Cassandra, time‑series and graph databases). /li liExperience with distributed compute/query engines (e.g., Spark, Trino/Presto, Databricks SQL) for large‑scale processing. /li liKnowledge of API and interoperability standards for data access (e.g., SQL, REST, GraphQL, gRPC, OpenAPI/AsyncAPI specifications). /li liExperience with semantic/metrics layers and BI modelling (e.g., dbt Semantic Layer, LookML, MetricFlow) to standardise KPIs. /li liUnderstanding of master and reference data management practices and tooling (e.g., Informatica MDM, Semarchy, Reltio). /li liExperience with data quality/observability tooling and SLAs/SLOs (e.g., Great Expectations, Soda, Monte Carlo) to monitor freshness, completeness, and lineage. /li liKnowledge of streaming and real‑time patterns (e.g., Spark Structured Streaming, Flink) and state stores (e.g., Kafka Streams). /li liExperience with DevSecOps/DataOps practices: version control, CI/CD for data, automated testing, and environment promotion (e.g., Git, GitHub/GitLab CI). /li liKnowledge of Infrastructure‑as‑Code and Policy‑as‑Code for data platforms (e.g., Terraform, Bicep, OPA