Data Engineering Lead
Eurisko · Beyrouth
وصف الوظيفة
About the role
We are looking for an experienced Data Engineering Lead to head our Data Engineering team and define the data foundations powering enterprise analytics, AI, and Generative AI solutions.
Key responsibilities
- Lead and mentor the Data Engineering team while establishing engineering standards and architecture guidelines.
- Define enterprise data architecture across Data Lakes, Data Warehouses, Data Marts, and Lakehouse platforms.
- Design and oversee ETL/ELT, batch and real‑time pipelines, CDC, data integration, and streaming architectures.
- Establish Data Governance covering data quality, lineage, metadata, cataloguing, ownership, classification, access control, security, and lifecycle management.
- Build the data foundation required for GenAI applications, including RAG, vector databases, embeddings, document ingestion, enterprise knowledge bases, semantic search, knowledge graphs, and AI context/memory layers.
- Define data modeling and semantic standards supporting BI, analytics, AI, and operational applications.
- Evaluate technologies and architectures while balancing scalability, performance, security, governance, cost, and maintainability.
Required profile
- Strong experience in Data Engineering and Data Architecture with proven team leadership.
- Deep knowledge of Data Warehouses, Data Lakes, Data Marts, and Lakehouse architectures.
Required skills
- Enterprise ETL platforms such as Informatica PowerCenter/IDMC, IBM DataStage, Talend, SSIS, Oracle Data Integrator.
- Big data and streaming technologies: Spark, Kafka, Databricks.
- Cloud data warehouses: Snowflake, Microsoft Fabric/Synapse, BigQuery, Redshift.
- Orchestration and transformation tools: Airflow, dbt.
- Strong SQL, data modeling, CDC, APIs, batch and streaming processing.
- Data governance and cataloguing tools: Informatica Data Governance & Catalog, Collibra, Microsoft Purview, Alation.
- Cloud platforms: Azure, AWS, GCP.
- GenAI data architectures: Retrieval‑Augmented Generation, vector search, embeddings, knowledge graphs.
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Eurisko
Beyrouth