Solution Architect
Skills
About This Role
Overview
Engineering Services are seeking a Solution Architect with hands-on expertise in data engineering and solution architecture.
This position bridges the gap between high-level business requirements and technical execution, defining the architecture of scalable data pipelines, robust storage solutions, and high-performance analytics environments.
The responsibilities include prototyping complex integrations and ensuring that data infrastructure is robust, secure, resilient, and optimized for scale-up.
As a practitioner in this role, the Solution Architect will be responsible for translating business needs into technical solutions, leveraging your expertise to drive the design and implementation of data-driven systems.
Key Responsibilities
- Architectural Design: Lead the design of end-to-end solution and data architectures, including ingestion, processing, storage, and visualization layers.
- Infrastructure Strategy: Define cloud-native or hybrid infrastructure strategies that support high-volume data processing and real-time analytics.
- Hands-on Development: Develop Proof of Concepts (PoCs) and core framework components for data pipelines (e.g. ETL) to ensure architectural feasibility.
- 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.
- Technical Leadership: Provide technical leadership in ensuring best practices in various areas including CI/CD and documentation.
- 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.
- Legacy Modernization: Experience migrating on-premise infrastructure to cloud-native architectures.
- Containerization: Experience with Docker and Kubernetes for deploying data services.
- 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.
- Enterprise Architecture: Knowledge of enterprise architecture principles, including business architecture, information architecture, and technology architecture.
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