Responsible for designing, developing, and optimizing the organization's data architecture to support business needs and analytics. This role involves creating scalable data models, defining data governance standards, and ensuring seamless data integration across platforms. He will have strong expertise in data engineering, database management, and modern data architectures, including cloud-based and big-data solutions.
Data Architecture & Design:
Develop and maintain a scalable, secure, and high-performance data architecture aligned with business needs.
Ensure seamless data flow between systems, integrating structured and unstructured data sources.
Define and implement best practices for data storage, processing, and security.
Select and optimize data platforms, tools, and technologies for efficiency and cost-effectiveness.
Data Modeling:
Design and implement data models (dimensional, relational, Data Vault, NoSQL) to support operational and analytical use cases.
· Ensure consistency, accuracy, and integrity of data models across the organization.
Continuously refine models to improve performance and adaptability to evolving business requirements.
Establish and enforce data modeling standards and governance.
Data Engineering:
· Build, optimize, and maintain automated ETL/ELT pipelines to process large-scale data.
· Enhance data infrastructure to support efficient storage, retrieval, and transformation.
· Implement automation to reduce manual processes and increase workflow efficiency.
Ensure data pipelines are robust, scalable, and support real-time and batch processing.
Innovation & Technology Adoption:
· Stay up to date with the latest trends in data architecture, engineering, and analytics to identify and implement innovative solutions.
· Research and integrate cutting-edge tools and platforms to enhance data management capabilities and streamline processes.
· Encourage a team culture focused on continuous improvement, supporting experimentation with new technologies and methodologies.
Develop and execute a technology roadmap to ensure the team is using the best tools available to meet both current and future data needs.
Optimization & Performance Management:
· Define and enforce data governance policies, ensuring compliance with GDPR, HIPAA, and other regulations.
· Monitor and optimize database performance, query execution, and storage efficiency.
· Manage data quality, metadata, and lineage to ensure reliability and transparency.
Implement proactive monitoring and alerting for data infrastructure health.
Educational Requirements: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.
Advanced degrees provide deeper technical and strategic insights, enabling the professional to drive complex data solutions and innovations at the organizational level.
Special Certification or Training Required:
- Certified Data Management Professional (CDMP) – Preferred for expertise in data architecture and governance is preferred.
- Google Professional Data Engineer or Microsoft Certified: Azure Data Engineer Associate is preferred.
Required Industry Experience:
- 3-5 years of experience in Data Architecture, Data Modeling, and Data Engineering with a proven track record of leading large-scale enterprise data initiatives.
- Expertise in designing, implementing, and optimizing complex data ecosystems in hybrid environments.
- Strong background in data governance, security, compliance, and risk management across enterprise systems.
- Hands-on experience managing data integration, migration, and transformation projects in a large-scale business setting
Technological Requirements:
- Expertise in data modeling tools (e.g., Erwin, IBM InfoSphere Data Architect, Microsoft Visio.
- Proficient in Data Vault modeling, dimensional modeling, and entity-relationship modeling.
- Proficient in both relational databases (e.g., Oracle, SQL Server, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra.
- Experience in data pipelines using Apache Spark, Apache Kafka, or Airflow.
- Strong programming skills in SQL, Python, and Java.
- Experience with shell scripting and automation frameworks for data engineering tasks.
- Familiarity with Hadoop, Apache Spark, and other big data tools for processing large datasets.
- Proficient in data lakes and data warehouses for big data environments
Language Requirements: Fluent in English