At Sparq , we help companies solve the right problems—not just build more technology.
We’re a modern product engineering partner blending strategy, craftsmanship, and speed to help organizations modernize confidently in the age of AI. From data ecosystems to digital products and AI acceleration, we turn complexity into clarity and ideas into impact.
If you’re driven to build what’s next, lead with empathy, and deliver excellence without ego, you’ll feel right at home at Sparq .
Why you will enjoy Mondays again:
- Opportunity to collaborate with a diverse group of colleagues in a fun, creative environment
- Progressive career journey and opportunity for advancement
- Continuous development through training, mentorship and certification programs
- Exposure to modern technologies across various industries in an agile environment
- Remote work
A Day in the Life:
- Design and evolve enterprise data architecture across lakehouse, data warehouse, pipeline, and semantic layers, ensuring scalability, performance, and alignment with enterprise standards
- Translate high-level architectural strategy into actionable technical plans, breaking work into epics, features, and sprint-ready stories for engineering teams
- Lead technical design discussions, proactively identifying dependencies, risks, and tradeoffs to drive efficient and predictable delivery
- Establish and enforce best practices for data modeling, pipeline design, data quality, and taxonomy across the platform
- Review architecture and code for complex data solutions, ensuring consistency, maintainability, and optimal performance
- Serve as a technical bridge across Data Engineering, Data Science, and Enterprise Architecture to ensure cohesive, end-to-end solutions
- Mentor engineers by providing architectural guidance and elevating system-level thinking across the team
- Design and build scalable data pipelines, dataflows, and semantic models using modern cloud-based data platforms (Microsoft Fabric / Azure)
- Contribute hands-on to high-impact initiatives, particularly where architecture and implementation are closely intertwined
- Support modernization efforts, including migration from on-prem SQL Server environments to cloud-based data platforms
- Optimize data processing workflows for performance, reliability, and cost efficiency using modern engineering patterns
- Partner with Data Scientists to productionize and operationalize machine learning models within enterprise data pipelines
- Participate in release planning and cross-team coordination to ensure aligned delivery across multiple initiatives
- Create and maintain clear, concise technical documentation covering architecture, standards, and key systems
What it takes:
- Deep expertise in modern data architectures, including lakehouse, data warehousing, data pipelines