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Job Description
- Design and develop end-to-end data architecture (Data Ingestion → Processing → Storage → Serving) to support Analytics, Business Intelligence (BI), and AI/ML initiatives.
- Build and optimize enterprise-scale Data Lake, Lakehouse, and Data Warehouse architectures aligned with business strategy.
- Design conceptual, logical, and physical data models across various business domains.
- Establish data standards, including naming conventions, data modeling standards, Slowly Changing Dimensions (SCD), Change Data Capture (CDC), partitioning strategies, schema evolution, and data contracts.
- Design and implement both real-time and batch data integration solutions using streaming technologies, APIs, and ELT/ETL pipelines.
- Evaluate, select, and drive the implementation of modern data platforms such as Databricks, Amazon Redshift, and Google BigQuery.
- Collaborate with the Data Engineering team to build scalable data pipelines, workflow orchestration, and CI/CD processes for data platforms.
- Define and oversee Data Governance practices, including data cataloging, data lineage, data quality, SLA management, and metadata management.
- Design and implement data security and access control mechanisms, including RBAC/ABAC, data masking, encryption, and regulatory compliance.
- Optimize the performance, scalability, cost efficiency, and reliability of cloud-based data platforms.
- Define the enterprise data architecture roadmap aligned with business objectives and digital transformation strategies.
- Conduct technical design reviews, mentor team members, and provide guidance in resolving complex technical challenges.
