FEQ325R106
This role can be remote.
As a Specialist Solutions Architect (SSA) - Data Warehousing, you will guide customers in their cloud data warehousing transformation with Databricks which span a large variety of use cases. You will be in a customer-facing role, working with and supporting Solution Architects, that requires hands-on production experience with large-scale data warehousing technologies and lakehouse architecture. SSAs help customers through evaluations and successful production planning for their business intelligence workloads while aligning their technical roadmap for 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, learning, and internal training programs and establish yourself in the data warehousing specialty - including performance tuning, data modeling, winning evaluations, architecture design, and production migration planning.
The impact you will have:
- Provide technical leadership to guide strategic customers to successful cloud transformations on large-scale data warehousing workloads - ranging from evaluation to architecture design to production deployment
- 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
- Become a technical expert in an area such as data warehousing evaluations or helping set up successful workload migrations
- Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing and performance, and tuning workloads for production
- Provide tutorials and training to improve community adoption (including hackathons and conference presentations)
- Contribute to the Databricks Community
What we look for:
- 5+ years experience in a technical role with expertise in data warehousing - such as query tuning, performance tuning, troubleshooting, data governance, debugging MPP data warehouses or other big data solutions, or migration workloads from EDWother systems
- Experience with design and implementation of data warehousing technologies including relational databases, SQL, data analytics, NoSQL, MPP, OLTP, and OLAP
- Deep Specialty Expertise in at least one of the following areas:
- Experience scaling large analytical data workloads in the cloud that are performant and cost-effective
- Maintained, extended, or migrated a production data warehouse system to evolve with complex needs, including data modeling, data governance needs, and integration with business intelligence tools
- Experience migrating on-premise EDW workloads to the public cloud
- Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience through work experience
- Production programming experience in SQL and Python, Scala, or Java
- Experience with the AWS, Azure, or GCP clouds
- 2 years of professional experience with data warehousing and big data technologies (Ex: SQL, Redshift, SAP, Synapse, EMR, OLAP & OLTP workloads)
- 2 years of customer-facing experience in a pre-sales or post-sales role
- Can meet expectations for technical training and role-specific outcomes within 6 months of hire
- Can travel up to 30% when 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 base 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 utilizes 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.
Zone 1 Pay Range
$139,800—$247,300 USD
Zone 2 Pay Range
$139,800—$247,300 USD
Zone 3 Pay Range
$139,800—$247,300 USD
Zone 4 Pay Range
$139,800—$247,300 USD
About Databricks
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, please visit https://www.mybenefitsnow.com/databricks.
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.
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.
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