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Solution Architect

Aramco

Chicago, IL 60290

Onsite

Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Systems, or a related field.
Minimum of 15 years in IT, with at least 4 years in a Solution Architect or Lead Data Engineer role.
Proven track record of delivering at least two large-scale enterprise data platform migrations or greenfield builds.
Cloud Infrastructure: Expert-level experience with at least one major cloud provider (AWS, Azure, or GCP) and their native data services.
Data Processing: Strong proficiency in batch and stream processing.
Programming: Advanced coding skills in Python, or Java, along with expert-level SQL.
Database Systems: Extensive experience managing and querying various systems: Relational: PostgreSQL, MySQL, SQL Server, NoSQL/NewSQL: MongoDB, Cassandra, Warehousing/Lakehouses: Snowflake, Databricks, BigQuery.
Containerization: Experience with Docker and Kubernetes for deploying data services.
Legacy Modernization: Experience migrating on-premise infrastructure to cloud-native architectures.
Solution Architecture: Develop and maintain a deep understanding of the corporate's technology landscape, including applications, data flows, and infrastructure, to identify opportunities for improvement and optimization.
Enterprise Architecture: Knowledge of enterprise architecture principles, including business architecture, information architecture, and technology architecture.
Technical Leadership: Provide technical leadership in ensuring best practices in various areas including CI/CD and documentation.
Database Governance: Evaluate and select appropriate data stores (Relational, NoSQL, Graph, Vector) based on specific use-case requirements like latency, throughput, and consistency.
Performance Optimization: Audit existing data systems to identify bottlenecks and implement strategies for cost-optimization and query performance tuning.
Infrastructure Strategy: Define cloud-native or hybrid infrastructure strategies that support high-volume data processing and real-time analytics.
Architectural Design: Lead the design of end-to-end solution and data architectures, including ingestion, processing, storage, and visualization layers.
Hands-on Development: Develop Proof of Concepts (PoCs) and core framework components for data pipelines (e.g. ETL) to ensure architectural feasibility.