Specialist Solutions Architect - Data Engineering & Warehousing (Digital Native Business)

FEQ227R367

As a Data Engineering and Warehousing Specialist Solutions Architect (SSA), you will lead the advanced technical strategy for your customers — owning complex architecture discussions, driving platform adoption, and serving as a trusted advisor to customer technical leads and architects. You combine deep technical expertise with strategic thinking to position Databricks as the foundation of your customers’ data and AI strategy. You are further developing a technical specialization and are recognized within the Field Engineering team for depth in the specific domain.

This position can be remote. 

The Impact You Will Have

• Own the end-to-end technical strategy for your accounts, from discovery through production deployment and consumption growth
• Lead complex architecture discussions — designing scalable, production-grade solutions spanning data engineering and real-time analytics
• Serve as a trusted technical advisor to customer architects, engineering leads, and Directors
• Drive technical wins in competitive scenarios by demonstrating Databricks’ differentiation through custom-built solutions
• Develop and declare an emerging technical specialization (archetype) — becoming a go-to resource for your team in that domain
• Orchestrate cross-functional resources (DSAs, SAs, Partners) to deliver comprehensive solutions for complex customer needs
• Influence product direction by providing structured feedback on customer requirements and competitive gaps

What We Look For

• 6+ years in solutions architecture, technical pre-sales, or a senior hands-on technical role in the following areas:
  
  • Data and Software Engineering: Deep hands-on experience with Apache Spark™ ecosystem (Spark Core, Spark SQL, Spark Streaming), message queues (e.g., Kafka), batch ingestion, performance tuning, and troubleshooting complex Spark workloads
  • Data Applications Engineering: Experience building or supporting data-driven use cases, predictive analytics pipelines, or customer analytics platforms
  • Data Warehousing & Migration: Experience migrating EDW workloads (e.g., legacy SQL, Redshift, Snowflake, Synapse, EMR) across OLAP/OLTP systems; advanced query tuning, governance, and MPP debugging
  • [Nice to have] Data Observability & Security: Telemetry, high-velocity log ingestion, anomaly detection, and familiarity with SIEM tools (e.g., Splunk, Elastic, Sentinel)
• Strong coding proficiency in Python and SQL — you must demonstrate live coding, debugging, and solution-building skills
• Deep expertise in distributed data systems architecture: designing scalable pipelines, streaming architectures, lakehouse patterns, and cloud-native data platforms
• Proficient on the Databricks Platform (or demonstrated ability to achieve proficiency rapidly) with a developing technical specialization in one area (e.g., real-time/streaming, ML/AI, data governance, migrations)
• Proven ability to lead architecture discussions with senior technical stakeholders — whiteboarding, design reviews, and trade-off analysis
• Experience with production deployments on public cloud (AWS, Azure, or GCP), including infrastructure, security, and governance considerations
• Track record of driving platform adoption and consumption growth within accounts
• Excellent communication skills — able to translate complex architectures into business value for both technical and executive audiences
• Bachelor's or Master's degree in Computer Science, Engineering, or a quantitative discipline (or equivalent experience)
• Willingness to travel up to 30% as needed

Nice to Have

• Databricks certifications (Data Engineer, ML, Platform)
• Experience with competitive platforms (Snowflake, AWS native services, Azure Synapse) — understanding the landscape you'll position against
• Background in a data/AI company or cloud provider
• Industry domain expertise (Financial Services, Healthcare, Retail, Media, etc.)