Specialist Solutions Architect - Data Engineering & Observability

FEQ327R257

As a Specialist Solutions Architect (SSA) - Data Engineering & Observability, you will guide customers through cloud data engineering transformations across a wide variety of use cases.

In this customer-facing role, you will collaborate with and support Solutions Architects. This requires hands-on production experience with large-scale data warehousing technologies and lakehouse architecture. The SSA teams help customers navigate evaluations and successful production planning for their business intelligence workloads while aligning their technical roadmap with the Databricks Data Intelligence Platform.

As a deep go-to expert reporting to the Specialist Field Engineering Manager, you will continue to strengthen your technical skills through mentorship, continuous learning, and internal training programs. In this role, you will establish yourself as a leader in the data engineering and warehousing specialty.

• Provide technical leadership to guide strategic customers to successful implementations on big data projects and large-scale data warehousing workloads.
  
• Prove the value of the Databricks Intelligence Platform for customer workloads by architecting production workloads, including end-to-end pipeline load performance testing and optimization.
• Architect production-level data pipelines, including end-to-end pipeline load performance testing and optimization.
• Become a technical expert in an area such as data lake technology, big data streaming, or big data ingestion and workflows.
• Assist Solution Architects with more advanced aspects of the technical sale, including custom proof of concept content, estimating workload sizing, and custom architectures.
• Provide tutorials and training to improve community adoption (including hackathons and conference presentations).
• Contribute to the Databricks Community.

What we look for:

• 5+ years of experience in a technical role with deep expertise across data engineering and data warehousing:
  • Software / Data Engineering: Hands-on experience with data ingestion, streaming technologies (e.g., Spark Streaming, Kafka), performance tuning, troubleshooting, and debugging Spark or other big data solutions.
  • Data Applications Engineering: Experience building data-driven use cases, such as risk modeling, fraud detection, and customer lifetime value (LTV).
  • Data Observability: Experience with SIEM tools (e.g., Splunk, Elastic, Sentinel), telemetry/high-velocity log ingestion, and anomaly detection.
• Proven track record of maintaining, scaling, and extending production data systems to evolve with complex business needs.
• Deep expertise across multiple core data engineering domains, including:
  • Designing and scaling cost-efficient, high-performance data workloads (ETL/ELT, analytics) in cloud environments.
  • Building and migrating large-scale data pipelines, including batch, CDC (Change Data Capture), and streaming ingestion.
  • Migrating on-premises or Hadoop-based data systems to modern cloud platforms (AWS, Azure, GCP).
    Developing and managing modern lakehouse and warehouse systems, including Delta Lake technologies, data modeling, governance, and BI integration.
• Production programming experience in SQL and at least one of the following: Python, Scala, or Java.
• Strong familiarity with cloud infrastructure providers (AWS, Azure, or GCP) is highly desirable.
• Degree or Equivalent: Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.
• [Preferred] Prior customer-facing experience in a pre-sales or post-sales technical role.
• Ability to meet expectations for technical training and role-specific milestones within 6 months of hire.
• Willingness to travel up to 30% as needed.

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.

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.

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.