Specialist Solutions Architect - Data Engineering & Warehousing (Digital Native Business)
FEQ327R656
As a Specialist Solutions Architect (SSA) – Data Engineering & Warehousing, you will guide strategic enterprise customers through cloud data engineering transformations across a wide variety of mission-critical use cases.
In this customer-facing role, you will collaborate with and support Solutions Architects by leveraging your hands-on production experience with large-scale data engineering and lakehouse architecture. You will help organizations navigate technical evaluations, optimize business intelligence and analytics workloads, and align their technical roadmaps with the Databricks Data Intelligence Platform.
Reporting to the Specialist Field Engineering Manager, you will serve as a deep domain expert while continuing to strengthen your technical leadership through mentorship, continuous learning, and specialized training programs.
This position can be remote.
The impact you will have:
- Guide Strategic Implementations: Provide technical leadership to help enterprise customers successfully build, scale, and optimize big data and large-scale data warehousing workloads.
- Prove Platform Value: Architect production-ready pipelines and demonstrate the power of the Databricks Data Intelligence Platform through end-to-end performance testing, load testing, and optimization.
- Deep Domain Expertise: Build expertise across specialized domains such as data lake architecture, high-velocity streaming, automated ingestion workflows, and data observability.
- Support Technical Sales: Partner with Solutions Architects on complex pre-sales engagements, including custom proofs of concept (POCs), workload sizing estimations, and custom architecture designs.
- Community & Adoption: Enable adoption by leading workshops, hackathons, and conference presentations, while actively contributing to the broader Databricks community.
What we look for:
- 5+ years of experience in a technical role with deep expertise across:
- Data & Software Engineering: Hands-on experience with streaming technologies (e.g., Spark Streaming, 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.
- Data Observability & Security: Telemetry, high-velocity log ingestion, anomaly detection, and familiarity with SIEM tools (e.g., Splunk, Elastic, Sentinel).
- Deep understanding of modern lakehouse architectures (Delta Lake, data modeling, BI integration) across major cloud platforms (AWS, Azure, or GCP).
- Production-level programming experience in SQL and at least one language among Python, Scala, or Java.
- [Preferred] Prior experience in a pre-sales or post-sales technical consulting role.
- Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent practical experience.
- Ability to hit role-specific training and technical delivery milestones within the first 6 months.
- Willingness to travel up to 30% as needed.
Pay Range Transparency
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.
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
- 5+ years in a technical role with deep expertise in data engineering and warehousing
- Hands-on experience with streaming technologies (e.g., Spark Streaming, Kafka)
- Experience with batch ingestion, performance tuning, and troubleshooting complex Spark workloads
- Experience migrating EDW workloads and advanced query tuning (Redshift, Snowflake, Synapse, EMR)
- Experience with data observability, telemetry, high-velocity log ingestion, and familiarity with SIEM tools (Splunk, Elastic, Sentinel)
- Deep understanding of modern lakehouse architectures (Delta Lake) across major cloud platforms (AWS, Azure, GCP)
- Production-level programming in SQL and at least one of: Python, Scala, or Java
- Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience
- Ability to hit role-specific training and technical delivery milestones within the first 6 months
- Willingness to travel up to 30% as needed
- Prior experience in a pre-sales or post-sales technical consulting role
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.
