Sr. Solutions Engineer - Games

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We need to parse the posting. First, remove boilerplate: cookie/navigation/legal/EEO etc. Also remove duplicated lines, application instructions, etc. We need to keep substantive info: description of role, responsibilities, requirements, qualifications, skills, benefits, pay range. We need to format with

headings and

• for lists,

  for prose. We must not repeat job title as heading. The title is "Sr. Solutions Engineer" maybe but we shouldn't output heading for it. We need to include sections only when there is real content. Let's extract content. Posting starts: "FEQ427R337 While candidates in the listed location(s) are encouraged for this role, candidates in other locations will be considered." That's metadata; we can fold into short

  maybe. Then: "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 company and role overview. Could be "About the role" or "About Databricks". We have later a section "About Databricks". We'll need to decide. Next: "As a Sr. Solutions Engineer, you will independently lead technical engagements for customers, owning discovery, solution design, and platform demonstrations. You are a builder who can code, architect, and present—combining technical depth with customer-facing skills to drive Databricks adoption. You will own frontline customer relationships and work with your Account Executive to develop technical strategies that expand platform usage." Then "The Impact You Will Have" bullet 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 That's responsibilities. Then "What We Look For" list: - 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) Then "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 Then "Interview Process: Recruiter Screen → Hiring Manager Screen → Design and Architecture Interview → Live Coding Assessment → Build, Demo, Pitch! Presentation → Reference Check #CMEG" Then "Pay Range Transparency" with zones and ranges. Then "About Databricks" paragraph. Then "Benefits" paragraph: "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." Then EEO etc which we need to remove. We need to include sections: About the role (maybe combine overview), Responsibilities, Requirements (or Qualifications), Skills, Benefits, Pay. We need to decide which headings to use. We have "What We Look For" which is requirements/qualifications. Could be "Requirements". Also "Nice To Have" could be "Preferred Qualifications" but we may not have a heading for that unless we include as part of Requirements or Skills. We have skills: proficiency in Python and SQL, etc. Could be under Skills. We have responsibilities list. We have benefits: short paragraph. We have pay: zone ranges. We have about the role: maybe combine initial paragraphs and As a Sr. Solutions Engineer paragraph. We need to avoid filler like "commensurate with experience". Not present. We need to output only HTML fragment. Let's structure:

  About the role

  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! As a Sr. Solutions Engineer, you will independently lead technical engagements for customers, owning discovery, solution design, and platform demonstrations. You are a builder who can code, architect, and present—combining technical depth with customer-facing skills to drive Databricks adoption. You will own frontline customer relationships and work with your Account Executive to develop technical strategies that expand platform usage.

  Then responsibilities:

  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
  Then Requirements/Qualifications: We could have

  Requirements

  with list of "What We Look For". Also maybe separate "Preferred Qualifications" for Nice To Have. But guidelines: Prefer a few substantive sections over many thin ones. Could combine Requirements and Nice To Have into one section? But they'd be separate lists. We could do

  Qualifications

  with two sub-lists? But we cannot create subheadings inside? We could just have one list with both required and nice-to-have but differentiate? Better to have two sections: Requirements and Preferred Qualifications. But guidelines: only include sections when posting has real specific content. Both have content. Thus:

  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)

  Preferred Qualifications

  • 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
  Then Skills: maybe we could incorporate skills into Requirements; but we have a separate Skills section maybe not needed. The guidelines: Use headings for Skills if we have content. We could treat the technical proficiencies as Skills. But we already have them in Requirements. Could combine. But we need to follow instruction: Use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have Requirements and Qualifications separate; but we already used Requirements. Could also have Skills separate listing technical skills. We could move the technical proficiencies (Python, SQL, Spark, etc.) to Skills section, and keep experience/education in Requirements. Let's think: The posting lumps them together; but we can split. Better to follow the example headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Thus we need sections for each if content exists. We have About the role (we have). Responsibilities (we have). Requirements (maybe years of experience, experience leading conversations, degree). Qualifications (maybe certifications, nice-to-have). Skills (technical proficiencies). Benefits (we have). Pay (we have). Schedule (none, so omit). Let's extract: Requirements content: "4+ years in data engineering, solutions architecture, technical pre-sales, or a hands-on consulting role", "Experience leading technical customer conversations — discovery, whiteboarding, architecture reviews", "Bachelor's or Master's degree in Computer Science, Engineering, or a quantitative discipline (or equivalent experience)". Also maybe "Hands-on experience designing and implementing data solutions on at least one public cloud platform (AWS, Azure, or GCP)" could be skill/experience but could be requirement. Qualifications: maybe "Proficient in Python and SQL with demonstrated ability to debug, optimize, and write production-quality code — live coding is a required interview stage", "Working knowledge of distributed data systems: Apache Spark™, Delta Lake, or equivalent (Hadoop, Kafka, Flink)", "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