Sr. Solutions Engineer - Games
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real content. We need to parse the posting. The posting includes: - Intro: "While candidates in the listed location(s) are encouraged for this role, candidates in other locations will be considered." This is location note; could be minor metadata; we can put in a short
maybe. - Then description of Databricks mission etc. That's about the role/company; could be "About the role" or "About Databricks". The instruction: use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have sections: About the role (or About Databricks?), Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay.
We need to preserve all substantive info, not invent.
Let's extract:
About the role: The mission description: "At Databricks, our core principles are at the heart of everything we do; creating a culture of proactiveness and a customer-centric mindset guides us to create a unified platform that makes data science and analytics accessible to everyone. We aim to inspire our customers to make informed decisions that push their business forward. We provide a user-friendly and intuitive platform that makes it easy to turn insights into action and fosters a culture of creativity, experimentation, and continuous improvement. You will be an essential part of this mission, using your technical expertise to demonstrate how our Data Intelligence Platform can help customers solve their complex data challenges. You'll work with a collaborative, customer-focused team that values innovation and creativity, using your skills to create customized solutions to help our customers achieve their goals and guide their businesses forward. Join us in our quest to change how people work with data and make a better world!"
That's about the role/company.
Responsibilities: The bullet list under "As a Sr. Solutions Engineer, you will independently lead technical engagements for customers, owning discovery, solution design, and platform demonstrations." Then list: "Independently lead technical discovery and solution design for customer workloads spanning data engineering, analytics, and machine learning", "Build and deliver compelling proofs-of-concept and live demos on the Databricks Platform that drive technical wins", "Own frontline technical relationships with customer engineers, data teams, and technical leads", "Develop account-level technical strategies in partnership with your Account Executive to grow platform consumption", "Navigate competitive landscapes by articulating Databricks differentiation through hands-on demonstrations", "Contribute reusable technical assets (notebooks, solution accelerators, reference architectures) to the broader SA community". Those are responsibilities.
What We Look For: This is requirements/qualifications/skills. It includes:
- 4+ years in data engineering, solutions architecture, technical pre-sales, or a hands-on consulting role
- Proficient in Python and SQL with demonstrated ability to debug, optimize, and write production-quality code — live coding is a required interview stage
- Hands-on experience designing and implementing data solutions on at least one public cloud platform (AWS, Azure, or GCP)
- Working knowledge of distributed data systems: Apache Spark™, Delta Lake, or equivalent (Hadoop, Kafka, Flink)
- Experience leading technical customer conversations — discovery, whiteboarding, architecture reviews
- Familiarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming), data science/ML (model training, MLOps), or SQL analytics
- Strong presentation and demo skills — you will build and present a live solution during the interview
- Bachelor's or Master's degree in Computer Science, Engineering, or a quantitative discipline (or equivalent experience)
Nice To Have: Databricks certification or experience with the Databricks Platform, Experience with Unity Catalog, Lakeflow Spark Declarative Pipelines, or MLflow, Background at a data/AI company, cloud provider, or technical consulting firm.
Interview Process: Recruiter Screen → Hiring Manager Screen → Design and Architecture Interview → Live Coding Assessment → Build, Demo, Pitch! Presentation → Reference Check #CMEG
Pay Range Transparency: includes zones with same range $152,300—$209,450 USD for zones 1-4. Also mention total compensation may include annual performance bonus, equity, benefits.
Benefits section: "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."
We need to include Benefits section only if posting has real specific content. It says "For specific details on the benefits offered in your region click here." That's not specific; it's a placeholder. According to rule: never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD"). This is similar: "For specific details on the benefits offered in your region click here." That's not specific benefits. So we should NOT include Benefits section because it's not substantive.
Pay section: we have specific pay ranges per zone. That is substantive. So include Pay section with Pay
and then maybe a
describing zones and range. Could also use
- for each zone? The rule: Use
- for any list of duties/requirements/benefits. Pay is not a list of duties/requirements/benefits, but could be a list. However we can just use
. It's okay. Schedule: no schedule info, so omit. We also need to include maybe a short
for location note: "While candidates in the listed location(s) are encouraged for this role, candidates in other locations will be considered." That's minor metadata; we can put in a
before sections. We must not repeat job title as heading. The title is Sr. Solutions Engineer but we shouldn't add heading. We must not include Job ID/requisition numbers as headings; we can drop. We must remove boilerplate: EEO, compliance, etc. Remove those. We must preserve all substantive info: include mission description, responsibilities, requirements (What We Look For), nice to have maybe as part of Requirements or Skills? Could separate Qualifications and Skills. The instruction list includes Qualifications, Skills sections. We could put "What We Look For" under Requirements (or Qualifications). Nice To Have could be under Skills or Qualifications as optional. But we need to avoid filler. We'll include sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Pay. We need to decide what goes where. Requirements: maybe the mandatory items (4+ years, Python/SQL, cloud experience, distributed systems, leading technical conversations, familiarity with one area, presentation skills, degree). Qualifications: maybe the Nice To Have? Or we could treat Requirements as mandatory, Qualifications as Nice To Have, Skills as technical skills (Python, SQL, Spark, etc). But we have overlapping. Better: Use Requirements for the mandatory bullet list. Use Qualifications for education (Bachelor's/Master's). Use Skills for technical skills (Python, SQL, Spark, cloud, presentation). But we need to avoid duplication. Let's extract: Mandatory requirements (What We Look For): - 4+ years in data engineering, solutions architecture, technical pre-sales, or a hands-on consulting role - Proficient in Python and SQL with demonstrated ability to debug, optimize, and write production-quality code — live coding is a required interview stage - Hands-on experience designing and implementing data solutions on at least one public cloud platform (AWS, Azure, or GCP) - Working knowledge of distributed data systems: Apache Spark™, Delta Lake, or equivalent (Hadoop, Kafka, Flink) - Experience leading technical customer conversations — discovery, whiteboarding, architecture reviews - Familiarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming), data science/ML (model training, MLOps), or SQL analytics - Strong presentation and demo skills — you will build and present a live solution during the interview - Bachelor's or Master's degree in Computer Science, Engineering, or a quantitative discipline (or equivalent experience) Nice To Have: - Databricks certification or experience with the Databricks Platform - Experience with Unity Catalog, Lakeflow Spark Declarative Pipelines, or MLflow - Background at a data/AI company, cloud provider, or technical consulting firm We could put Nice To Have under Skills or Qualifications. But we have a Skills section; we could list technical skills: Python, SQL, Spark, Delta Lake, cloud platforms, presentation/demo, etc. However the Nice To Have items are more like extra qualifications. Given the instruction to prefer a few substantive sections over many thin ones, we could combine Requirements and Qualifications into one section "Requirements" that includes both mandatory and nice-to-have? But we need to follow headings list: we can use Requirements, Qualifications, Skills. But we can omit Qualifications if we put education under Requirements. And we can put Skills as a separate section listing technical skills. Let's decide: Section About the role: mission description. Section Responsibilities: list of duties. Section Requirements: include mandatory items (years, experience, education, etc.) maybe also nice-to-have? Could include nice-to-have as part of Requirements but label as "Nice to have". However we should not invent headings; we can just list them as bullet points under Requirements. Section Skills: maybe list technical skills: Python, SQL, Spark, Delta Lake, cloud platforms (AWS/Azure/GCP), presentation/demo skills, etc. But we already have those in Requirements. Might be redundant. Given the instruction to prefer few substantive sections, maybe we just have About the role, Responsibilities, Requirements, Pay. That's four sections. That seems sufficient. We need to include all substantive info: mission description, responsibilities, requirements (including nice-to-have), pay. We can put education under Requirements. Thus:
About the role
...mission description...
Responsibilities
- Independently lead technical discovery and solution design for customer workloads spanning data engineering, analytics, and machine learning
- Build and deliver compelling proofs-of-concept and live demos on the Databricks Platform that drive technical wins
- Own frontline technical relationships with customer engineers, data teams, and technical leads
- Develop account-level technical strategies in partnership with your Account Executive to grow platform consumption
- Navigate competitive landscapes by articulating Databricks differentiation through hands-on demonstrations
- Contribute reusable technical assets (notebooks, solution accelerators, reference architectures) to the broader SA community
Requirements
- 4+ years in data engineering, solutions architecture, technical pre-sales, or a hands-on consulting role
- Proficient in Python and SQL with demonstrated ability to debug, optimize, and write production-quality code — live coding is a required interview stage
- Hands-on experience designing and implementing data solutions on at least one public cloud platform (AWS, Azure, or GCP)
- Working knowledge of distributed data systems: Apache Spark™, Delta Lake, or equivalent (Hadoop, Kafka, Flink)
- Experience leading technical customer conversations — discovery, whiteboarding, architecture reviews
- Familiarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming), data science/ML (model training, MLOps), or SQL analytics
- Strong presentation and demo skills — you will build and present a live solution during the interview
- Bachelor's or Master's degree in Computer Science, Engineering, or a quantitative discipline (or equivalent experience)
- Nice to have: Databricks certification or experience with the Databricks Platform
- Nice to have: Experience with Unity
- for any list of duties/requirements/benefits. Pay is not a list of duties/requirements/benefits, but could be a list. However we can just use