BlackStone eIT
BlackStone eIT
BlackStone eIT एक वैश्विक प्रौद्योगिकी और परामर्श कंपनी है, जो डिजिटल रूपांतरण, बुद्धिमान डिज़ाइन और बेहतर ग्राहक सहभागिता पर केंद्रित एंटरप्राइज़ समाधान विकसित करती है। इसका कार्य संवर्धित वास्तविकता, ब्लॉकचेन, बुद्धिमान चैटबॉट, भावनात्मक स्कैनिंग, ऑप्टिकल कैरेक्टर रिकग्निशन, रोबोटिक्स, चेहरे और वस्तु की पहचान तथा स्मार्ट एनालिटिक्स जैसे क्षेत्रों में फैला है। कंपनी अरबी प्राकृतिक भाषा प्रसंस्करण इंजन, डिजिटल कार्यस्थल, चेहरे की उपस्थिति प्रणालियों और शेड्यूलिंग प्लेटफ़ॉर्म जैसे अनुकूलित उपकरण भी प्रदान करती है। BlackStone eIT परामर्श, विपणन और लॉजिस्टिक्स सहित विभिन्न क्षेत्रों के संगठनों को सेवाएँ देती है और उभरती प्रौद्योगिकियों को व्यावहारिक व्यावसायिक अनुप्रयोगों के साथ जोड़ती है।

Lead Data Architect (Hybrid, Egypt)

Lead enterprise data architecture, governance, cloud platforms, and migration programs for an Egypt-based organization. Guide data teams and shape scalable solutions across analytics, integration, and AI/ML initiatives.

विवरण

  • Own the enterprise data architecture strategy, standards, and roadmap in line with business priorities.
  • Create scalable conceptual, logical, and physical models for OLTP, OLAP, data lake, and lakehouse environments.
  • Design data pipelines, ETL/ELT workflows, and integration patterns spanning source systems, warehouses, and downstream applications.
  • Assess and choose appropriate data platforms, tools, and technologies.
  • Establish and apply frameworks for data governance, quality, master data, and metadata management.
  • Lead and develop data architects, engineers, and analysts.
  • Evaluate and approve data architecture designs, models, and technical implementations.
  • Provide technical direction for data security, privacy, access management, and compliance.
  • Promote consistent practices for modeling, naming, version control, and documentation.
  • Work with business, product, and engineering stakeholders to turn requirements into data architecture solutions.
  • Coordinate with BI, analytics, data science, and application teams on reporting, analytics, and AI/ML use cases.
  • Manage data migration, legacy platform retirement, and cloud migration initiatives.
  • Enable integrations with ERP, CRM, and third-party systems.
  • Contribute to architecture review boards and provide technical approval for significant data initiatives.
  • Implement processes for monitoring and resolving data quality issues.
  • Maintain data catalogs, lineage records, and metadata repositories.
  • Ensure architecture meets scalability, performance, security, and cost-efficiency objectives.

आवश्यकताएँ

  • Bring 8–10 years of experience in data architecture, data engineering, or a related field, including 3–5 years in a lead or architect role.
  • Demonstrate hands-on expertise in dimensional, star, snowflake, normalized, and denormalized data modeling.
  • Have deep experience with at least one major cloud data platform, such as AWS Redshift or Glue, Azure Synapse or Data Factory, GCP BigQuery or Dataflow, Snowflake, or Databricks.
  • Use SQL confidently and have experience with ETL/ELT tools such as Informatica, Talend, dbt, or Azure Data Factory.
  • Understand data warehouse, data lake, and/or lakehouse architecture patterns.
  • Know the principles and practices of data governance, master data management, data quality, and metadata management.
  • Have worked with API-based and event-driven integrations, including Kafka, REST, or OIC.
  • Translate business requirements into practical technical architectures while managing stakeholders effectively.
  • Have experience leading and mentoring technical teams.
  • Be familiar with BI and reporting platforms such as Power BI, Tableau, OBIEE, or Looker.
  • Understand data pipeline and feature store requirements for AI and machine learning workloads.
  • Know data security and compliance frameworks including GDPR, HIPAA, and data residency requirements.
  • Relevant certifications in AWS, Azure, or GCP data architecture, Snowflake, Databricks, DAMA, or CDMP are applicable.
  • Bring experience in an industry relevant to the organization, such as finance, healthcare, government, or retail.
  • Program in Python or Scala for data engineering work.
  • Combine strategic judgment, strong technical depth, business understanding, leadership, mentoring, structured problem solving, and clear communication with technical and non-technical stakeholders.

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