Data Platform Engineer
About The Position
As a Data Platform Engineer at Quik!, you will be a cornerstone of our data architecture. You’ll design, implement, and evolve scalable data systems that serve our product platform, internal engineering, and next-gen AI features. This role directly enables Quik!’s mission to deliver intuitive, intelligent forms automation. You will work closely with our Backend Engineers, DevOps, Product Owners, and the CTO to ensure performance, scalability, and availability of data systems while embracing modern tooling, cloud-native infrastructure, and AI-first practices.
Requirements
- 5+ years of experience managing or building large-scale data systems
- Proficient in SQL, NoSQL, and distributed systems (MSSQL, MongoDB, Redis)
- Comfortable with Elasticsearch or Solr, schema evolution, and data modeling
- Familiar with cloud-native infrastructure (AWS or Azure)
- Skilled in observability and performance tuning
- Excited to adopt AI-assisted tools to boost efficiency and quality
Nice To Haves
- Experience with vector databases and semantic search
- Knowledge of RAG patterns or AI-first architecture
- Familiarity with streaming/event-driven platforms like Kafka or EventBridge
- Experience with Kubernetes or container-based deployments
Responsibilities
- Design and maintain scalable storage across SQL, NoSQL, in-memory, and search platforms
- Ensure system resilience, availability, and observability
- Implement lifecycle management, backup/recovery, and security protocols
- Identify and resolve bottlenecks across the data layer
- Tune indexing, caching, and partitioning strategies
- Improve query throughput and data latency for AI and transactional workloads
- Support AI-driven features with performant, accessible data structures
- Work on vector storage, semantic indexing, and future RAG (Retrieval-Augmented Generation) features
- Use AI-assisted tools (e.g., GitHub Copilot, ChatGPT) to improve development speed and documentation
- Partner with engineering teams to deliver backend features reliant on complex data pipelines
- Document schemas, systems, and best practices
- Mentor peers on observability and modern data design
Benefits
- Health coverage
- paid time off
- professional development
- home office stipend
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
Number of Employees
11-50 employees
