Data Architect

Tata Consultancy Services; Brussel Hoofdstad; Sur place; EUR 90 000 - 120 000; Plein temps

Résumé du poste

Tata Consultancy Services Belgium is seeking a Senior Data Architect to design and implement a holistic data strategy. You will translate business needs into scalable data architectures and oversee metadata, governance, and security across on‑premise and cloud environments.

Lead data modelling, integration, and streaming pipelines, collaborate with engineers and data scientists, and champion compliant, cost‑efficient solutions in a GDPR context.

Qualifications

• Advanced understanding of enterprise data architecture methods (DAMA-DMBOK, Data Mesh, Data Fabric).
• Proficient in data modelling: 3NF, star, Data Vault 2.0; tools like ERwin/SAP PowerDesigner.
• Experience with metadata/cataloguing platforms (Collibra, Alation, Purview, OpenLineage).
• Familiar with data governance/quality frameworks (ISO 8000, ISO/IEC 11179).
• Knowledge of privacy/security/compliance (GDPR, ISO/IEC 27001); RBAC/ABAC principles.

Responsabilités

• Develop and implement the organization's data strategy aligned with business objectives.
• Translate business requirements into data architecture and designs.
• Create conceptual/logical/physical data models and metadata repositories.
• Architect data integration across ERPs/CRMs using ETL/ELT pipelines.
• Build streaming pipelines (Kafka, Spark Streaming) for real-time analytics.

Connaissances

Data architecture

Data governance

ETL/ELT

Streaming data

Cloud platforms

GDPR compliance

Formation

Bachelor's/Master's in Computer Science or related field

Outils

Collibra

Azure Purview

OpenLineage

Snowflake

Databricks

Description du poste

Location: Brussels, Belgium

Company: Tata Consultancy Services (TCS) Belgium

Employment Type: Full-time

Nature of the Tasks

• Develop and implement the organization's overarching data strategy, creating blueprints for data management that align with and enable key business objectives.
• Translate business requirements into technical specifications and data architecture designs, ensuring the data infrastructure supports both immediate and long-term needs.
• Create conceptual, logical, and physical data models (e.g., dimensional for analytics) that define data structure, relationships, and storage.
• Maintain metadata repositories to ensure data accuracy, lineage, and integration, curating both technical and business metadata for clarity.
• Architect solutions to integrate data from disparate sources (ERPs, CRMs) using ETL/ELT processes and tools (e.g., Apache NiFi, Talend) into a unified framework.
• Build and manage streaming data pipelines (e.g., using Kafka, Spark Streaming) to support real-time analytics and decision-making.
• Define and enforce data governance policies, including data quality standards, lineage tracking, access controls, and a data catalog.
• Implement security protocols (encryption, RBAC) and design architectures to ensure adherence to regulations like GDPR.
• Implement processes for data profiling, validation, and cleansing to ensure ongoing data accuracy, consistency, and reliability.
• Evaluate and select appropriate database systems (SQL, NoSQL), cloud platforms (AWS, Azure, GCP), and tools that meet scalability and performance needs.
• Architect and deploy scalable data solutions in cloud (e.g., Snowflake) or hybrid environments, optimizing for cost-efficiency.
• Monitor, troubleshoot, and optimize data systems and pipelines for performance, scalability, and cost.
• Work with business leaders, data engineers, and scientists to ensure the architecture meets diverse needs and bridges technical and non-technical gaps.
• Mentor data teams on best practices, standards, and tools; lead data-centric projects and strategic initiatives.
• Oversee the entire data lifecycle, from collection and storage to archiving and purging, ensuring data remains manageable and relevant.
• Stay abreast of trends in big data, AI, and cloud computing to continuously innovate and modernize the data architecture.

Specific Expertise and Technologies

• Knowledge of enterprise data architecture methods and reference models (e.g., DAMA-DMBOK, Data Mesh principles, Data Fabric patterns).
• Knowledge 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).
• Experience with metadata and cataloguing platforms to govern lineage and ownership (e.g., Collibra, Alation, Azure Purview, OpenLineage).
• Knowledge of data governance and quality frameworks (e.g., ISO 8000, ISO/IEC 11179), including stewardship, data domains, and controls.
• Understanding of privacy, security, and compliance requirements (e.g., GDPR, ISO/IEC 27001), including encryption, key management, RBAC/ABAC, and data residency.
• Experience with integration patterns and pipelines: ETL/ELT, CDC, event streaming (e.g., Kafka, Debezium) and orchestration (e.g., Airflow, Azure Data Factory, Dagster).
• Knowledge of lakehouse and warehouse architectures, table/format standards (e.g., Delta Lake, Apache Iceberg, Apache Hudi) and columnar formats (e.g., Parquet).
• Experience with cloud data platforms such as Azure Synapse, Databricks, Microsoft Fabric, Snowflake, and Amazon Redshift.
• Knowledge of relational and NoSQL data stores and when to apply them (e.g., PostgreSQL, Oracle, MongoDB, Cassandra, time‑series and graph databases).
• Experience with distributed compute/query engines (e.g., Spark, Trino/Presto, Databricks SQL) for large‑scale processing.
• Knowledge of API and interoperability standards for data access (e.g., SQL, REST, GraphQL, gRPC, OpenAPI/AsyncAPI specifications).
• Experience with semantic/metrics layers and BI modelling (e.g., dbt Semantic Layer, LookML, MetricFlow) to standardise KPIs.
• Understanding of master and reference data management practices and tooling (e.g., Informatica MDM, Semarchy, Reltio).
• Experience with data quality/observability tooling and SLAs/SLOs (e.g., Great Expectations, Soda, Monte Carlo) to monitor freshness, completeness, and lineage.
• Knowledge of streaming and real‑time patterns (e.g., Spark Structured Streaming, Flink) and state stores (e.g., Kafka Streams).
• Experience with DevSecOps/DataOps practices: version control, CI/CD for data, automated testing, and environment promotion (e.g., Git, GitHub/GitLab CI).
• Knowledge of Infrastructure‑as‑Code and Policy‑as‑Code for data platforms (e.g., Terraform, Bicep, OPA) to ensure repeatable, governed deployments.
• Understanding of backup/restore, disaster recovery, and retention strategies with RPO/RTO targets for data platforms.
• Experience with cost and performance optimisation across compute, storage, and egress (e.g., workload right‑sizing, caching/partitioning, lifecycle policies).
• Knowledge of collaboration and documentation practices (e.g., ADRs, C4 context for data flows, glossary/business term management).
• Understanding of AI/analytics enablement: feature stores and model data needs (e.g., Feast), responsible AI data controls aligned to EU AI Act principles.

Minimum Level of Expertise

• Advanced

Certification and/or Standards

Mandatory: One of the following or an equivalent certification:

• TOGAF
• CDMP
• DAMA‑DMBoK
• ISO Data Governance Standards

Optional:

• Cloud Data Certifications (AWS, Azure, etc.)
• ITIL 4 Foundation
• SAFe
• Security Certifications (e.g., CCSP, CISSP)

Skills

• Capacity to leverage storytelling in data architecture communications.
• Ability to synthesize long‑term business objectives with technical feasibility to guide project vision and validate architectural decisions.
• Ability to understand, speak, and write English; French is considered an additional asset.
• Ability to work both independently and as part of a team.
• Ability to participate in multilingual meetings.
• Excellent interpersonal and communication skills.
• Results‑oriented mindset focused on delivering outcomes.