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As a Specialist Solutions Architect (SSA), you will guide customers in building big data solutions on Databricks that span a large variety of use cases. These are customer-facing roles, working with and supporting the Solution Architects, requiring hands-on production experience with Apache Spark™ and expertise in other data technologies. SSAs help customers through design and successful implementation of essential workloads while aligning their technical roadmap for expanding the usage of the Databricks Lakehouse Platform. As a deep go-to-expert reporting to the Director of Field Engineering, you will continue to strengthen your technical skills through mentorship, learning, and internal training programs and establish yourself in an area of specialty - whether that be performance tuning, machine learning, industry expertise, or more.
The impact you will have:
- Provide technical leadership to guide strategic customers to successful implementations on big data projects, ranging from architectural design to data engineering to model deployment
- Architect production level workloads, including end-to-end pipeline load performance testing and optimisation
- Provide technical expertise in an area such as data management, cloud platforms, data science, machine learning, or architecture
- Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing, and custom architectures
- Improve community adoption (through tutorials, training, hackathons, conference presentations)
- Contribute to the Databricks Community
What we look for:
- You will have experience in a customer-facing technical role with expertise in at least one of the following:
- Software Engineer/Data Engineer: query tuning, performance tuning, troubleshooting, and debugging Spark or other big data solutions.
- Data Scientist/ML Engineer: model selection, model lifecycle, hyper parameter tuning, model serving, deep learning.
- Data Applications Engineer: Build use cases that use data - such as risk modelling, fraud detection, customer life-time value.
- Experience with design and implementation of big data technologies such as Spark/Delta, Hadoop, NoSQL, MPP, OLTP, and OLAP.
- Maintain and extend production data systems to evolve with complex needs.
- Production programming experience in Python, R, Scala or Java
- Deep Specialty Expertise in at least one of the following areas:
- Experience scaling big data workloads that are performant and cost-effective.
- Experience with Development Tools for CI/CD, Unit and Integration testing, Automation and Orchestration, REST API, BI tools and SQL Interfaces.
- Experience designing data solutions on cloud infrastructure and services, such as AWS, Azure, or GCP using best practices in cloud security and networking.
- Experience with ML concepts covering Model Tracking, Model Serving and other aspects of productionizing ML pipelines in distributed data processing environments like Apache Spark, using tools like MLflow.
- Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
- Native level Korean is required and Business level English is a plus
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Skills Required
- Customer-facing technical experience in software/data engineering, data science/ML engineering, or data applications engineering
- Hands-on production experience with Apache Spark and Databricks Lakehouse Platform
- Experience designing and implementing big data technologies (Spark/Delta, Hadoop, NoSQL, MPP, OLTP, OLAP)
- Production programming experience in Python, R, Scala or Java
- Deep specialty expertise in at least one area (performance tuning, ML, data management, cloud architecture, industry domain)
- Experience scaling big data workloads for performance and cost-effectiveness
- Experience with CI/CD, unit/integration testing, automation/orchestration, REST APIs, BI tools and SQL interfaces
- Experience designing data solutions on cloud platforms (AWS, Azure, GCP) including cloud security and networking best practices
- Experience with ML production concepts (model tracking, model serving) and tools like MLflow
- Maintain and extend production data systems to meet evolving requirements
- Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
- Native level Korean language ability
- Business level English
Databricks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Databricks and has not been reviewed or approved by Databricks.
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Equity Value & Accessibility — Equity grants are a meaningful part of offers, and periodic tender opportunities and secondary options have made private equity more tangible for many employees. This perceived upside contributes to strong total-compensation sentiment in key roles.
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Healthcare Strength — Comprehensive medical, dental, and vision coverage is paired with mental‑health resources and wellness reimbursements, indicating a robust health package. Multiple summaries highlight broad coverage that employees can practically use.
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Leave & Time Off Breadth — Generous PTO, paid holidays/sick time, and fully paid parental leave are frequently described, with hybrid/remote flexibility common in the U.S. These policies expand time‑off accessibility across different life stages.
Databricks Insights
What We Do
As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
