What you will do
- Develop and implement enterprise-wide data strategies and architecture blueprints aligned with business objectives and long-term organizational goals.
- Translate business requirements into technical specifications, data architecture designs, and scalable data management solutions.
- Design and maintain conceptual, logical, and physical data models, including dimensional modelling, Star Schema, and enterprise data structures.
- Establish and maintain metadata repositories to ensure data accuracy, lineage, ownership, traceability, and integration across enterprise systems.
- Design and implement data integration architectures to consolidate data from multiple sources, including ERP and CRM systems, using ETL/ELT processes and tools.
- Architect and manage real-time and streaming data pipelines using technologies such as Apache Kafka, Spark Streaming, and related frameworks.
- Define and enforce enterprise data governance policies covering data quality, data lineage, data cataloguing, access controls, and data ownership.
- Implement data security and privacy controls, including encryption, Role-Based Access Control (RBAC), and compliance with GDPR and other regulatory requirements.
- Establish data profiling, validation, cleansing, and quality monitoring processes to ensure data consistency, accuracy, and reliability.
- Evaluate and select appropriate database technologies, cloud platforms, and data management tools based on scalability, security, and performance requirements.
- Architect and deploy scalable cloud-based and hybrid data platforms using technologies such as Snowflake, Azure Synapse, Databricks, and Microsoft Fabric.
- Monitor, troubleshoot, and optimize enterprise data systems and pipelines for performance, scalability, availability, and cost efficiency.
- Collaborate with business leaders, Data Engineers, Data Scientists, and technical teams to ensure data architecture aligns with business and analytical requirements.
- Lead data architecture initiatives, establish technical standards, and mentor data teams on architecture best practices and governance frameworks.
- Oversee the complete data lifecycle, including data collection, storage, processing, retention, archiving, and deletion.
- Define enterprise data architecture standards and continuously modernize data platforms by evaluating emerging technologies in Big Data, AI, and cloud computing.
- Establish disaster recovery, backup, and data retention strategies with clearly defined Recovery Point Objectives (RPOs) and Recovery Time Objectives (RTOs).
What you bring
- Strong experience in Enterprise Data Architecture, Data Strategy, and large-scale data platform design.
- Advanced knowledge of enterprise data architecture frameworks and methodologies such as DAMA-DMBOK, Data Mesh, and Data Fabric.
- Strong understanding of data privacy, security, and compliance requirements, including GDPR and ISO/IEC 27001.
- Knowledge of encryption, key management, RBAC, ABAC, and data residency requirements.
- Experience designing data integration architectures using ETL/ELT, Change Data Capture (CDC), and event-streaming technologies.
- Hands-on knowledge of Apache Kafka, Debezium, Apache NiFi, Talend, Apache Airflow, Azure Data Factory, and Dagster.
- Strong understanding of modern Data Lake, Data Warehouse, and Lakehouse architectures.
- Experience with data storage formats and technologies such as Delta Lake, Apache Iceberg, Apache Hudi, and Parquet.
- Experience with cloud data platforms such as Azure Synapse, Databricks, Microsoft Fabric, Snowflake, and Amazon Redshift.
- Strong knowledge of relational and NoSQL databases, including PostgreSQL, Oracle, MongoDB, and Cassandra.
- Experience with distributed processing and query engines such as Apache Spark, Trino, Presto, and Databricks SQL.
- Understanding of data interoperability and API standards, including SQL, REST, GraphQL, gRPC, OpenAPI, and AsyncAPI.
- Experience with semantic layers, metrics management, and BI modelling tools such as dbt Semantic Layer, LookML, and MetricFlow.
- Knowledge of Master Data Management (MDM) and Reference Data Management solutions such as Informatica MDM, Semarchy, and Reltio.
- Experience with data quality and observability tools such as Great Expectations, Soda, and Monte Carlo.
- Knowledge of real-time data processing technologies such as Spark Structured Streaming, Apache Flink, and Kafka Streams.
- Experience with DataOps and DevSecOps practices, including Git, CI/CD pipelines, automated testing, and environment promotion.
- Knowledge of Infrastructure as Code and Policy as Code tools such as Terraform, Bicep, and Open Policy Agent (OPA).
- Understanding of backup, recovery, disaster recovery, data retention, and cost optimization strategies.
- Familiarity with architecture documentation practices, including Architecture Decision Records (ADRs), C4 diagrams, data flow documentation, and business glossaries.
- Understanding of AI and analytics enablement, feature stores such as Feast, and responsible AI data governance aligned with EU AI Act principles.
How you work
- Translate complex business objectives into scalable, secure, and sustainable enterprise data architecture solutions.
- Define long-term data strategies while balancing business priorities, technical feasibility, and architectural constraints.
- Lead enterprise data architecture initiatives and make effective architectural decisions.
- Communicate complex data architecture concepts through clear documentation, diagrams, and data storytelling.
- Evaluate emerging data technologies and recommend appropriate solutions based on business and technical requirements.
- Establish enterprise-wide data governance, data quality, security, and compliance standards.
- Collaborate effectively with business stakeholders, architects, Data Engineers, Data Scientists, and leadership teams.
- Identify architectural risks, integration challenges, and performance bottlenecks and recommend appropriate solutions.
- Mentor technical teams and promote best practices in data architecture, modelling, governance, and platform engineering.
- Work independently as well as collaboratively within international and multicultural teams.
- Participate effectively in multilingual meetings and communicate fluently in English; French is an additional advantage.
- Demonstrate excellent interpersonal, stakeholder management, analytical, and communication skills.
- Maintain a proactive, flexible, and results-oriented approach focused on delivering business outcomes.
What you can expect
- Opportunities to lead enterprise-level data architecture and transformation initiatives.
- Exposure to modern cloud data platforms, Big Data technologies, and enterprise data governance solutions.
- Opportunities to design scalable Data Lake, Data Warehouse, and Lakehouse architectures.
- Collaboration with senior business stakeholders, enterprise architects, Data Engineers, and Data Scientists.