Specialist Solutions Architect - Data Engineering & Warehousing
Leads technical strategy and architecture for customers adopting Databricks data engineering and warehousing solutions. Requires 6+ years in solutions architecture or a senior technical role, strong Spark, Python, SQL, distributed systems, and public cloud expertise.
About the job
Responsibilities
- Own end-to-end technical strategy for accounts, from discovery through production deployment and consumption growth.
- Lead complex architecture discussions and design scalable, production-grade solutions spanning data engineering 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 domain resource for the Field Engineering team.
- Coordinate DSAs, solutions architects, and partners to deliver comprehensive solutions.
- Provide structured customer and competitive feedback to influence product direction.
Requirements
- 6+ years in solutions architecture, technical pre-sales, or a senior hands-on technical role.
- Deep hands-on experience with the Apache Spark ecosystem, including Spark Core, Spark SQL, and Spark Streaming.
- Experience with message queues, batch ingestion, performance tuning, and troubleshooting complex Spark workloads.
- Experience building or supporting data-driven use cases, predictive analytics pipelines, or customer analytics platforms.
- Experience migrating enterprise data warehouse workloads across OLAP and OLTP systems, including query tuning, governance, and MPP debugging.
- Strong coding proficiency in Python and SQL, including live coding, debugging, and solution building.
- Expertise in distributed data systems architecture, scalable pipelines, streaming architectures, lakehouse patterns, and cloud-native data platforms.
- Proficiency with the Databricks Platform or ability to become proficient rapidly.
- 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.
- Track record of driving platform adoption and consumption growth within accounts.
- Excellent communication skills for translating complex architectures into business value.
- Bachelor's or master's degree in Computer Science, Engineering, or a quantitative discipline, or equivalent experience.
- Willingness to travel up to 30%.
Nice to Have
- Databricks certifications in data engineering, machine learning, or platform technologies.
- Experience with Snowflake, AWS native services, or Azure Synapse.
- Data observability and security experience, including telemetry, high-velocity log ingestion, anomaly detection, Splunk, Elastic, or Sentinel.
- Background at a data/AI company or cloud provider.
- Industry domain expertise in financial services, healthcare, retail, media, or related sectors.
Compensation
- Local pay range: $180,000–$247,500 USD.
- Total compensation may also include an annual performance bonus, equity, and benefits.
Skills
Solutions Architecture, Spark, Spark Sql, Spark Streaming, Apache Kafka, Python, SQL, Data Warehousing, Real-Time Analytics, Data Pipelines, Lakehouse, Databricks, AWS, Microsoft Azure, GCP
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