Senior Solutions Engineer supporting enterprise sales for Sprout Social’s social intelligence platform. Demonstrating SaaS solutions and translating customer goals into actionable outcomes.
About the role
- Data Solution Architect designing SGI’s cloud data platforms across Azure and AWS. Enabling governed analytics, AI/ML, integration, and enterprise reporting solutions.
Responsibilities
- Design and architect modern, scalable, composable data platforms across AWS and Azure
- Assess data ecosystems and develop modernization roadmaps for transitions from on premises to cloud
- Implement architectural standards, governance practices, and reference architectures
- Evaluate and recommend data storage and compute technologies based on performance, scalability, cost, and security
- Design and optimize batch and streaming ETL/ELT pipelines using tools such as dbt, Airflow, Kafka, and Pub/Sub
- Develop architectural guidance for AI/ML data pipelines, feature stores, real-time inference, and MLOps alignment
- Support LLM and generative AI integration through vector stores, embeddings, pipelines, and model-serving infrastructure
- Provide strategic advice to business and technical stakeholders on data architecture and transformation goals
- Create reference architectures, integration strategies, migration plans, and governance models
- Partner with business, analytics, technology, IT, and Data Office teams to evolve data architecture and reporting frameworks
- Review the data landscape to identify risks, technical debt, and modernization opportunities
- Contribute technical expertise to RFP requirements, response evaluations, and recommendations
- Ensure data solutions are actionable, implementable, secure, and aligned with approved architecture standards
- Identify technology interactions and dependencies affecting integration, performance, scalability, and compatibility
- Support enterprise data initiatives, platform enhancements, and production operations
Requirements
- Four-year degree from an accredited post-secondary education institution in a relevant field, such as Computer Science, Information Technology, Mathematics & Applied Science, or Engineering, or defined equivalency
- Six years’ experience in data engineering and enterprise data solutions, with progressive involvement in data architecture, Azure and cloud-based data platforms, and data integration initiatives
- Knowledge of enterprise data architecture principles, including Lakehouse, Medallion, Data Vault, and Data Mesh architectures
- Knowledge of data modeling, including data gathering, inspection, cleansing, and transformation
- Knowledge of solution design, database design and management, application integration, agile implementation, and cloud-based platforms
- Knowledge of Azure data platform components, including Azure Data Lake, Cosmos DB, Synapse Analytics, Databricks, Azure Data Factory, Blob Storage, and Azure Stream
- Knowledge of Snowflake, Delta Lake, Apache Iceberg, Amazon Redshift, and emerging data ecosystem tools
- Knowledge of cloud cost management and optimization strategies
- Knowledge of Property and Casualty industry data, products, and services
- Skill in data modeling and architecture tools, including physical implementation, design implications, and performance requirements
- Ability to translate business and architectural requirements into implementable data solutions
- Ability to design scalable, cost-effective, secure, and integrated data solutions aligned with business strategy
Benefits
- Hybrid work option in Regina
- Remote work may be considered for candidates residing outside Regina but within Saskatchewan
- Opportunities for growth
- Professional and career development
- Mentoring, training and developmental opportunities
- Inclusive hiring practices and workplace belonging
- Recruitment accommodation support
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