Data Engineering Specialist Solutions Architect - Lakehouse

Databricks is the Data and AI company. Over 20,000 organizations worldwide—including 70% of the Fortune 500—rely on the Databricks Data + AI Platform to build and scale data and AI applications, analytics, and autonomous agents.

As a Data Engineering and Warehousing Specialist Solutions Architect (SSA), you will lead the advanced technical strategy for your enterprise accounts—owning complex architectural design, driving consumption growth, and serving as a trusted technical advisor to Directors, Enterprise Architects, and Data Engineering Leads. Combining deep technical expertise with strategic pre-sales execution, you will architect large-scale batch and streaming data pipelines, migrate legacy Enterprise Data Warehouse (EDW) workloads (Redshift, Snowflake, Synapse, legacy SQL) to the Databricks Lakehouse, optimize Apache Spark™ performance, and demonstrate live coding proficiency in Python and SQL.

Key Responsibilities

• Enterprise Technical Strategy: Own the end-to-end technical strategy across strategic accounts—from discovery and proof-of-value (PoV) through production deployment and consumption scaling.
• Data Engineering & Lakehouse Architecture: Design scalable, production-grade architectures spanning real-time analytics, batch ingestion, streaming pipelines (Apache Kafka, Spark Streaming), and Lakehouse data patterns.
• EDW Migration & Optimization: Guide enterprise clients through migrating legacy EDW workloads (Redshift, Snowflake, Azure Synapse, EMR) to Databricks; perform advanced query tuning, governance design (Unity Catalog), and MPP debugging.
• Competitive Technical Wins: Drive technical victories in competitive evaluation scenarios by whiteboarding custom-built solutions and proving Databricks’ architectural differentiation.
• Domain Specialization Lead: Develop and declare a deep technical archetype within Field Engineering (e.g., real-time/streaming, ML/AI integrations, governance, or migrations) to serve as a regional subject matter expert.
• Cross-Functional Collaboration & Product Feedback: Partner with Domain SAs, Account Executives, and Partner teams to deliver holistic solutions while providing structured field feedback to Databricks product management.

Requirements

Data Engineering, Warehousing & Pre-Sales Depth

• 6+ years of professional experience in solutions architecture, technical pre-sales, or senior hands-on data engineering architecture roles.
• Data & Software Engineering Fundamentals: Deep hands-on experience with the Apache Spark™ ecosystem (Spark Core, Spark SQL, Structured Streaming), message queues (Kafka), batch ingestion, performance tuning, and low-level Spark troubleshooting.
• Data Warehousing & Migration Mastery: Proven track record migrating legacy EDW architectures (Snowflake, Redshift, Synapse, legacy SQL) to cloud-native platforms, including MPP query optimization and data governance.
• Live Coding Proficiency: Demonstrated live coding, debugging, and solution-building skills in Python and SQL.
• Public Cloud Deployments: Experience architecting production deployments across public cloud platforms (AWS, Azure, or GCP), incorporating cloud security, networking, and governance baselines.
• Technical Advisory & Communication: Exceptional whiteboarding, design review, and presentation skills to bridge complex distributed architecture with executive business outcomes.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a quantitative discipline (or equivalent practical experience).
• Travel Logistics: Willingness to travel up to 30% for client whiteboarding sessions and team alignment.

Preferred Qualifications

• Recognized Databricks Certifications (Databricks Certified Data Engineer Professional, ML Professional, or Platform Administrator).
• Familiarity with data observability, telemetry, high-velocity log ingestion, and SIEM platforms (Splunk, Elastic, Sentinel).
• Deep competitive knowledge of Snowflake, AWS native data services, and Azure Synapse Analytics.
• Domain expertise in specific verticals such as Financial Services, Healthcare/Life Sciences, Retail, or Media.

Selection & Interview Process

1. Recruiter Screen
2. Hiring Manager Screen
3. Design & Architecture Interview
4. Live Coding Assessment (Python & SQL)
5. Technical Presentation
6. Reference Check

Total Rewards & Benefits

• Compensation & Equity: Pay range of $180,000 – $247,500 USD (On-Target Earnings) + annual performance bonus eligibility + Databricks equity grants.
• Healthcare & Wellbeing: Comprehensive medical, dental, and vision insurance coverage for employees and eligible dependents.
• Financial & Retirement: 401(k) plan options, life/disability coverage, and employee wellness stipends.
• Time Off & Flexibility: Flexible PTO, paid company holidays, paid sick leave, and parental leave.