Define and own the enterprise data architecture, data strategy, standards, and technical roadmap.
Design scalable data platforms, data lakes, data warehouses, and lakehouse architectures.
Develop conceptual, logical, and physical data models aligned with business and technical requirements.
Architect modern cloud data solutions using AWS, Azure, or Google Cloud Platform.
Design and oversee ETL/ELT pipelines and data integration across enterprise applications and data sources.
Establish data governance, data quality, metadata management, lineage, security, and privacy standards.
Evaluate and recommend databases and data technologies including Snowflake, Databricks, SQL Server, Oracle, PostgreSQL, and NoSQL platforms.
Design high-performance and highly available data architectures capable of handling large-scale data workloads.
Define integration strategies using APIs, event-driven architecture, messaging, and streaming technologies such as Kafka.
Partner with data engineers, architects, developers, DevOps teams, and business stakeholders to translate requirements into technical solutions.
Provide technical leadership, architecture reviews, design standards, and mentoring to data engineering teams.
Drive modernization of legacy data platforms toward cloud-native and modern data architectures.
Ensure solutions meet enterprise requirements for security, compliance, scalability, performance, and cost optimization.

Required Skills

15+ years of experience in data architecture, data engineering, or related technology roles.
Extensive experience designing enterprise data architecture and data platforms.
Strong expertise in data modeling, database design, and data warehousing.
Hands-on experience with cloud data platforms such as Azure, AWS, or Google Cloud Platform.
Strong knowledge of Snowflake, Databricks, Azure Data Factory, AWS Glue, Redshift, or equivalent technologies.
Advanced SQL and experience with relational and NoSQL databases.
Strong experience with ETL/ELT, data integration, and data pipeline architecture.
Experience with data governance, master data, metadata, data quality, and data lineage.
Knowledge of Kafka, APIs, event-driven architecture, and real-time data processing.
Experience with Python, Spark/PySpark, or other modern data engineering technologies.
Strong understanding of data security, privacy, access control, and regulatory compliance.
Excellent communication, leadership, architecture documentation, and stakeholder-management skills.

Preferred Qualifications