Data Architect

• 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.

• 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 standardize 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 optimization 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.