Senior Data Platform Architect

We need to output HTML fragment with sections only if content present. Use

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. The posting includes Job Summary (maybe About the role). Responsibilities section present. Job Requirements (maybe Requirements). Also "Nice to have / strong plus" could be Skills? Might be Skills. Benefits and Pay: there is "What We Offer You" with compensation, bonus, 401k, ESPP, benefits, vacation, holidays, cruise discount, gym membership, discounts, etc. Also schedule: hybrid position based in Woodland Hills, CA office, required to adhere to hybrid work policy, working from office Monday and Thursday plus one additional weekday weekly. That's schedule info. Thus we need sections: About the role (Job Summary), Responsibilities, Requirements (Job Requirements), Skills (Nice to have / strong plus), Benefits (What We Offer You), Pay (salary range etc), Schedule (hybrid work policy). Ensure we don't include empty sections. We must not repeat job title. No Job ID. Remove boilerplate like EEO etc. Not present. We must preserve all substantive info. Use

• for lists. Use

  for prose. We need to extract content. Let's parse. Job Summary paragraph: "We are looking for a Senior Data Platform Architect with strong end-to-end data and platform skills to design, operate, and secure the Azure Databricks that powers finance, revenue management, marketing, and booking and reservation across the business. This architect will report to Director, Customer Data and Identity Engineering. Beyond design, this role will cover the platform administration in North America including Unity Catalog governance, access and credentials, pipeline reliability, and cost — and turns it into something documented, automated. You will also set the standard for how AI-assisted coding development is used across the data team, and define the tooling that makes it safe. This is a hybrid position based in our Woodland Hills, CA office. You will be required to adhere to our hybrid work policy, working from our office Monday and Thursday plus one additional weekday on a weekly basis." We can split: About the role maybe first part up to hybrid position? But schedule part is separate. We'll keep About the role as the summary excluding schedule? But schedule is separate section. We'll include schedule later. Thus About the role content: everything before "This is a hybrid position..." maybe. Let's extract: "We are looking for a Senior Data Platform Architect with strong end-to-end data and platform skills to design, operate, and secure the Azure Databricks that powers finance, revenue management, marketing, and booking and reservation across the business. This architect will report to Director, Customer Data and Identity Engineering. Beyond design, this role will cover the platform administration in North America including Unity Catalog governance, access and credentials, pipeline reliability, and cost — and turns it into something documented, automated. You will also set the standard for how AI-assisted coding development is used across the data team, and define the tooling that makes it safe." That seems good. Responsibilities: list of bullet points under "Job Responsibilities". The text after that heading includes many paragraphs each starting with a bold heading? Actually the text: "Job ResponsibilitiesEnd-to-end platform architecture and implementationDesign the medallion architecture: catalog and schema design, layer contracts, and the promotion path from development through QA to production.Design robust, scalable data platform architectures aligned with enterprise security and networking standards.Act as a bridge between Data Solutions and Infrastructure, ensuring the platform decisions you make are the ones BI, AI, data science, and activation teams can actually build on.Databricks & Unity Catalog administrationAdminister Unity Catalog: catalog bindings, external locations, storage credentials, grants, and the service-principal model behind them.Define workspace and compute standards — cluster policies, runtime versions, pool lifecycle, job compute versus all-purpose, and serverless where it pays for itself.Manage platform identity end to end: users, groups, SCIM provisioning from Entra ID, secret scopes, and token governance.Keep the estate current: runtime upgrades, deprecation tracking, and retiring the pools and jobs that quietly stop working.Ingestion, orchestration & integrationDesign pipeline architecture across Azure Data Factory and Databricks Workflows: dependency design, idempotency, watermarking, and retry and backfill semantics that survive a bad source day.Keep source integrations healthy — Dataverse and Synapse Link exports, finance and booking systems, third-party feeds — including the runtime, connector, and credential lifecycle they silently depend on.Establish data contracts with upstream owners so schema changes reach the platform before they reach production.Land a modernization decision for SQL Server to Databricks without breaking the reporting built on it. AI-assisted engineering standards & harness enablementEstablish and enforce the AI coding standard for the data team: what assistants may generate, what must be human-reviewed before merge, and what may never reach production without a test behind it.Build and maintain the AI coding harness — repository instruction files, project context, MCP servers, tool permissions, and sandboxed environments — so generated code arrives already matching our conventions.Establish review practice for generated code and SQL: correctness against the data model, cost and performance impact, and provenance recorded in the pull request.Continuously evaluate and improve output quality, refining instructions and context based on what the team actually gets wrong — treating the harness as a product with users, not a config file.AI/ML platform administrationAdminister ML assets in Unity Catalog — registered models, aliases, feature tables, and MLflow experiments — including the grants and promotion path from development through QA to production.Define GPU and ML compute standards: node-type allowlists, Azure quota management, idle termination, spot strategy, and ML Runtime version lifecycle, so expensive compute cannot be left running unnoticed.Govern foundation model and external LLM access through the AI Gateway — rate limits, usage tracking, payload logging, guardrails, and provider credentials held in secret scopes rather than notebooks.Extend network and data-protection controls to AI workloads: egress rules for external model APIs, retention of logged prompts and responses, and handling of customer data in inference payloads.Security, access & platform governanceDefine the platform's security posture: network isolation, private connectivity, storage firewalls, and a documented rationale for every public-access exception that remains.Run credential hygiene — secrets into Key Vault, scheduled rotation, and no long-lived plaintext tokens in notebooks, pipelines, or configuration.Design and administer the access model: role definitions, joiner/mover/leaver flow, privileged access, and periodic entitlement review.Work with the Infrastructure team and move platform configuration into Terraform and CI/CD and keep it there, so there is no drift between what is deployed and what is declared.Identity resolution & marketing activationContribute to a consent, suppression, and preference management solution, ensure rules are applied correctly and provably per market, and that a failure in that logic is caught by the platform rather than by a customer.Steward the data-sharing connections into Adobe Experience Platform and activationReliability, quality & costDefine SLAs for the pipelines the business actually depends on, then build the monitoring and alerting that proves you are meeting them.Lead incident response for platform failures: triage, root cause, remediation, and a written RCA that changes something. Close the class of failure that hurts most — silent partial loads, duplicate or missing records, and jobs that report success on incomplete data.Support the data quality framework: rule coverage, severity semantics, and making sure a blocking rule actually blocks. Monitor platform spend: cluster right-sizing, storage lifecycle, and a defensible view of cost per workload." We need to turn each bullet into

• . The sections are separated by headings like "End-to-end platform architecture and implementation", "Databricks & Unity Catalog administration", etc. We could treat each heading as a subsection but we only need a single Responsibilities section with list items. Could group under headings but not required. We'll just create a list of responsibilities, each sentence as an item. We need to split at each period that starts a new responsibility? The text is dense. We'll break by the headings and then each sentence after colon? Simpler: create list items for each distinct responsibility phrase separated by periods. Might be many items but okay. We'll produce list items: - Design the medallion architecture: catalog and schema design, layer contracts, and the promotion path from development through QA to production. - Design robust, scalable data platform architectures aligned with enterprise security and networking standards. - Act as a bridge between Data Solutions and Infrastructure, ensuring the platform decisions you make are the ones BI, AI, data science, and activation teams can actually build on. - Administer Unity Catalog: catalog bindings, external locations, storage credentials, grants, and the service-principal model behind them. - Define workspace and compute standards — cluster policies, runtime versions, pool lifecycle, job compute versus all-purpose, and serverless where it pays for itself. - Manage platform identity end to end: users, groups, SCIM provisioning from Entra ID, secret scopes, and token governance. - Keep the estate current: runtime upgrades, deprecation tracking, and retiring the pools and jobs that quietly stop working. - Design pipeline architecture across Azure Data Factory and Databricks Workflows: dependency design, idempotency, watermarking, and retry and backfill semantics that survive a bad source day. - Keep source integrations healthy — Dataverse and Synapse Link exports, finance and booking systems, third-party feeds — including the runtime, connector, and credential lifecycle they silently depend on. - Establish data contracts with upstream owners so schema changes reach the platform before they reach production. - Land a modernization decision for SQL Server to Databricks without breaking the reporting built on it. - Establish and enforce the AI coding standard for the data team: what assistants may generate, what must be human-reviewed before merge, and what may never reach production without a test behind it. - Build and maintain the AI coding harness — repository instruction files, project context, MCP servers, tool permissions, and sandboxed environments — so generated code arrives already matching our conventions. - Establish review practice for generated code and SQL: correctness against the data model, cost and performance impact, and provenance recorded in the pull request. - Continuously evaluate and improve output quality, refining instructions and context based on what the team actually gets wrong — treating the harness as a product with users, not a config file. - Administer ML assets in Unity Catalog — registered models, aliases, feature tables, and MLflow experiments — including the grants and promotion path from development through QA to production. - Define GPU and ML compute standards: node-type allowlists, Azure quota management, idle termination, spot strategy, and ML Runtime version lifecycle, so expensive compute cannot be left running unnoticed. - Govern foundation model and external LLM access through the AI Gateway — rate limits, usage tracking, payload logging, guardrails, and provider credentials held in secret scopes rather than notebooks. - Extend network and data-protection controls to AI workloads: egress rules for external model APIs, retention of logged prompts and responses, and handling of customer data in inference payloads. - Define the platform's security posture: network isolation, private connectivity, storage firewalls, and a documented rationale for every public-access exception that remains. - Run credential hygiene — secrets into Key Vault, scheduled rotation, and no long-lived plaintext tokens in notebooks, pipelines, or configuration. - Design and administer the access model: role definitions, joiner/mover/leaver flow, privileged access, and periodic entitlement review. - Work with the Infrastructure team and move platform configuration into Terraform and CI/CD and keep it there, so there is no drift between what is deployed and what is declared. - Contribute to a consent, suppression, and preference management solution, ensure rules are applied correctly and provably per market, and that a failure in that logic is caught by the platform rather than by a customer. - Steward the data-sharing connections into Adobe Experience Platform and activation. - Define SLAs for the pipelines the business actually depends on, then build the monitoring and alerting that proves you are meeting them. - Lead incident response for platform failures: triage, root cause, remediation, and a written RCA that changes something. - Close the class of failure that hurts most — silent partial loads, duplicate