Specialist Solutions Architect - Data Engineering & Warehousing (Financial Services)
Databricks2 months ago
Base Salary
$180k - $248k/yr
Responsibilities
- Own end-to-end technical strategy for customer accounts from discovery through production deployment and consumption growth.
- Lead complex architecture discussions and design scalable, production-grade data engineering and real-time analytics solutions.
- Serve as a trusted technical advisor to customer architects, engineering leads, directors, and executive audiences.
- Drive technical wins and platform adoption through custom-built Databricks solutions.
- Develop a technical specialization and become a go-to resource within the Field Engineering team.
- Coordinate DSAs, SAs, and partners to deliver comprehensive solutions for complex customer needs.
- Provide structured customer and competitive feedback to influence product direction.
Requirements
- At least 6 years of experience in solutions architecture, technical pre-sales, or a senior hands-on technical role.
- Deep hands-on experience with Apache Spark, including Spark Core, Spark SQL, Spark Streaming, message queues, batch ingestion, performance tuning, and troubleshooting complex 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.
- Deep expertise in distributed data systems, scalable pipelines, streaming architectures, lakehouse patterns, and cloud-native data platforms.
- Proficiency with the Databricks Platform or ability to become proficient rapidly, with a developing specialization such as streaming, ML/AI, governance, or migrations.
- Experience leading architecture discussions, whiteboarding, design reviews, and trade-off analysis with senior technical stakeholders.
- Experience deploying systems in AWS, Azure, or GCP, including infrastructure, security, and governance considerations.
- Track record of driving platform adoption and consumption growth within customer 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% as needed.
- Preferred qualifications include Databricks certifications, experience with Snowflake, AWS native services, or Azure Synapse, a background in a data/AI company or cloud provider, and relevant industry expertise.
Benefits
- Remote work arrangement.
- Comprehensive benefits and perks, with details varying by region.
- Eligibility for an annual performance bonus and equity may be included in the total compensation package.
- Travel of up to 30% may be required.
Tech Stack
Amazon RedshiftApache KafkaApache SparkAWSAzureDatabricksGoogle Cloud PlatformPythonSnowflakeSplunkSQL
Categories
Solutions Engineering
About Databricks
Databricks builds a cloud-based data and AI platform centered on the lakehouse architecture, combining data engineering, analytics, and machine learning with Apache Spark, Delta Lake, and MLflow. It sells subscriptions and cloud services to enterprises that need to unify data pipelines and develop large-scale AI and analytics. Founded in 2013 by the creators of Apache Spark and headquartered in San Francisco, the company is privately held and serves organizations across many industries.