Solutions Architect
Leads technical strategy and architecture for customer data and AI solutions, guiding adoption from discovery through production. Requires 6+ years in solutions architecture or a senior technical role, strong Python and SQL skills, and expertise in cloud-native distributed data systems.
About the job
Responsibilities
- Own end-to-end technical strategy for accounts, from discovery through production deployment and consumption growth.
- Lead architecture discussions and design scalable, production-grade solutions across data engineering, ML/AI, and real-time analytics.
- Advise customer architects, engineering leads, and directors on technical strategy.
- Demonstrate platform differentiation through custom-built solutions in competitive scenarios.
- Develop a technical specialization and serve as a team resource in that domain.
- Coordinate DSAs, SSAs, and partners to deliver comprehensive customer solutions.
- Provide structured feedback on customer requirements and competitive gaps to influence product direction.
Requirements
- 6+ years of experience in solutions architecture, data engineering, technical presales, or a senior hands-on technical role.
- Strong Python and SQL coding proficiency, including live coding, debugging, and solution building.
- Deep expertise in distributed data systems, scalable pipelines, streaming architectures, lakehouse patterns, and cloud-native data platforms.
- Proficiency with the Databricks Platform, or the ability to become proficient rapidly, with a developing specialization in an area such as real-time/streaming, ML/AI, data governance, or migrations.
- Experience leading architecture discussions with senior technical stakeholders, including whiteboarding, design reviews, and trade-off analysis.
- Production deployment experience on AWS, Azure, or Google Cloud, including infrastructure, security, and governance considerations.
- Track record of driving platform adoption and consumption growth within accounts.
- Excellent communication skills for translating complex architectures into business value for technical and executive audiences.
- Bachelor's or master's degree in Computer Science, Engineering, or a quantitative discipline, or equivalent experience.
Nice to Have
- Databricks certifications in data engineering, ML, or platform technologies.
- Experience with Snowflake, AWS native services, or Azure Synapse.
- Background in a data/AI company or cloud provider.
- Industry expertise in financial services, healthcare, retail, media, or similar sectors.
Benefits
- Comprehensive benefits and perks; regional details vary.
Skills
Python, SQL, Databricks, Data Engineering, Machine Learning, Real-Time Analytics, Distributed Systems, Streaming Architectures, Lakehouse, AWS, Microsoft Azure, GCP, Snowflake, Azure Synapse
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