Resident Solution Architect

We need to output HTML fragment with sections: About the role (job summary), Responsibilities, Requirements Qualifications Skills Benefits Pay Schedule only if present. The posting includes Job Summary (we can treat as About the role). Responsibilities present. Qualifications present. Required Skills present (maybe treat as Skills). Preferred Skills also present. There's also a "Skills:" line at end with list. We need to combine skills? The instruction: Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use

• for any list of duties/requirements/benefits, and

  for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

  or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones. Thus we need sections: About the role (job summary), Responsibilities, Qualifications, Skills (maybe combine required and preferred). There's no explicit Requirements separate; but we have Qualifications and Required Skills. Could treat Qualifications as a section, and Skills as another. There's no Benefits, Pay, Schedule mentioned. So omit those. We must preserve all substantive information. Need to extract content. Job Summary paragraph: "We are seeking a Senior Solution Architect to join our Databricks Practice and play a pivotal role in shaping data modernization journeys for enterprise customers. This is a customer-facing, consulting-oriented role where you will engage with CDOs, Data Architects, and Engineering leaders to design and deliver scalable, future-ready Lakehouse solutions on the Databricks Data Intelligence Platform. You will be the trusted technical advisor — translating business problems into architectural blueprints, guiding customers through EDW modernization programs, and championing the adoption of Databricks' most advanced capabilities including Lakeflow Declarative Pipelines (LDP), Lakeflow Connect, Serverless Compute, Unity Catalog, and AI/BI workloads." Responsibilities: list bullet points after "Responsibilities:" line. Need to capture each bullet. The text after Responsibilities: appears as a long paragraph with sentences separated by periods. We need to split into list items. Let's parse: "Lead pre-sales and delivery architecture conversations with enterprise customers, owning the technical narrative from discovery through solution design and through implementation oversight. Design end-to-end Databricks Lakehouse architectures spanning ingestion, transformation, governance, ML, and consumption layers — aligned to Medallion Architecture principles. Drive EDW modernization engagements, including migration strategies from legacy platforms (Teradata, Netezza, Oracle, SAS, Greenplum, Synapse, Snowflake, on-prem Hadoop) to Databricks, with clear wave planning, risk mitigation, and TCO/ROI articulation. Advise customers on the adoption of advanced Databricks features. Lakeflow Declarative Pipelines (LDP / formerly DLT) for production-grade ETL with built-in data quality and lineage. Lakeflow Connect for managed ingestion from SaaS, databases, and file sources. Serverless Compute (SQL warehouses, jobs, notebooks) for cost and operational efficiency. Unity Catalog for unified governance, lineage, and fine-grained access control. Databricks Asset Bundles (DAB) for CI/CD and environment promotion Databricks SQL, AI/BI Dashboards, Genie for self-service analytics Expertise in Data Bulid Tool (DBT) and Databricks Autoloader Databricks SQL, AI/BI Dashboards, Genie for self-service analytics MLflow, Model Serving, and Mosaic AI for ML and GenAI workloads Conduct architecture deep-dives, PoCs, and workshops — including cost modelling roadmaps, runbooks and executive presentations. Produce Client ready deliverables. HLD / LLD documents, reference architecture, migration road maps, runbook and execution presentations Partner with sales, delivery, and Databricks field teams to shape proposals, SoWs, and bid responses. Stay ahead of the Databricks product roadmap and evangelize new capabilities internally and with customers. Mentor data engineers, platform engineers, and junior architects on Databricks best practices." Note there are duplicates: "Databricks SQL, AI/BI Dashboards, Genie for self-service analytics" appears twice. We should keep both? Probably keep as separate items but could deduplicate? The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. Duplicated lines: we should remove duplicated lines (they said remove duplicated lines). So we need to eliminate duplicate lines. So keep only one instance of that line. Also "Expertise in Data Bulid Tool (DBT) and Databricks Autoloader" appears as a line; keep. Now Qualifications: after "Qualifications" line: "8+ years in data engineering / data platform architecture, with 3+ years handson on Databricks in a customer-facing or consulting capacity. Demonstrated experience leading at least 2–3 large- scale EDW modernization or Lakehouse migration programs end to end. Strong, fundamentals-level understanding of the Databricks Data Intelligence Platform. Delta Lake internals (transaction log, OPTIMIZE, Z- order, Liquid Clustering, Deletion Vector, Predictive Optimization) Spark execution model, Photon, cluster sizing, and performance tuning. Unity Catalog object model (metastore, catalogs, schemas, volumes, external locations, stor age credentials). Workspace, account, and identity architecture across AWS / Azure / GCP. Handson experience designing and deploying Lakeflow Declarative Pipelines, Lakeflow Conn ect, and Serverless workloads in production. Solid grounding in Medallion Architecture, dimensional modeling, and data governance fra meworks. Strong SQL and PySpark skills; comfort reading and reasoning about Spark execution plans. Cloud fluency on at least one of Azure, AWS, or GCP including networking, IAM, and storage layers (ADLS Gen2 / S3 / GCS). Excellent communication and stakeholder management skills able to engage equally with engineers and C- suite. Willing to travel and stay overseas for Solutioning and Delivering projects." Note some typos with spaces; we keep as is. Required Skills: after "Required Skills" line: "Databricks certifications: Certified Data Engineer Professional, Certified Machine Learning Pr ofessional, or Databricks Certified Solutions Architect Professional. Experience with DR architectures (Delta Deep Clone, crossregion replication, RPO/RTO design). Exposure to MLOps / GenAI patterns on Databricks (MLflow, Model Serving, Vector Search, Mosaic AI Agent Framework) Familiarity with legacy ETL/ELT platforms (Informatica, ODI, SAS DI, DataStage, Talend) for migration credibility. Experience with Terraform / Databricks Asset Bundles for IaC and CI/CD. Working knowledge of Immuta, Collibra, Purview, or Alation for governance integration. Industry depth in BFSI, Insurance, Retail, Manufacturing, or Healthcare." Preferred Skills: after "Preferred Skills" line: "Owned the architecture for at least 2 strategic customer engagements from discovery to golive. Established yourself as the goto advisor on advanced Databricks adoption (LDP, Lakeflow Co nnect, Serverless) within the practice Contributed to reusable accelerators, reference architectures, and migration playbooks that scale across the practice. Built strong working relationships with Databricks field teams and contributed to joint p ursuits." Then there is a "Skills:" line at end: "Skills: mlops,databricks certified solutions architect,edw,bfsi, insurance, retail, manufacturing, or healthcare,etl". This seems like a tag list; we could incorporate into Skills section maybe as additional items. But we already have Required Skills and Preferred Skills sections; we could combine them into a single Skills section with sub-sections? The instruction: Use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use

  • for any list of duties/requirements/benefits, and

    for prose. So we can have a Skills section with list items (maybe combine required and preferred). However we also have Qualifications separate. The posting has Qualifications and Required Skills separate; we could keep Qualifications as a section and Skills as another (combining required and preferred). That seems reasonable. We must not create sections with filler. So we will have:

    About the role

    Job summary paragraph.

    Responsibilities

    • ...
    ...

    Qualifications

    • ...
    ...

    Skills

    • ...
    ...
    (combining required and preferred and the final Skills line). We need to ensure we preserve all substantive information. Let's extract each item. Responsibilities items (deduplicated): 1. Lead pre-sales and delivery architecture conversations with enterprise customers, owning the technical narrative from discovery through solution design and through implementation oversight. 2. Design end-to-end Databricks Lakehouse architectures spanning ingestion, transformation, governance, ML, and consumption layers — aligned to Medallion Architecture principles. 3. Drive EDW modernization engagements, including migration strategies from legacy platforms (Teradata, Netezza, Oracle, SAS, Greenplum, Synapse, Snowflake, on-prem Hadoop) to Databricks, with clear wave planning, risk mitigation, and TCO/ROI articulation. 4. Advise customers on the adoption of advanced Databricks features. - Lakeflow Declarative Pipelines (LDP / formerly DLT) for production-grade ETL with built-in data quality and lineage. - Lakeflow Connect for managed ingestion from SaaS, databases, and file sources. - Serverless Compute (SQL warehouses, jobs, notebooks) for cost and operational efficiency. - Unity Catalog for unified governance, lineage, and fine-grained access control. - Databricks Asset Bundles (DAB) for CI/CD and environment promotion - Databricks SQL, AI/BI Dashboards, Genie for self-service analytics - Expertise in Data Bulid Tool (DBT) and Databricks Autoloader - MLflow, Model Serving, and Mosaic AI for ML and GenAI workloads But note the original list had each as separate bullet lines after "Advise customers on the adoption of advanced Databricks features." The lines start with those capabilities. We could treat each as separate list items under Responsibilities. However the instruction: Use
    • for any list of duties/requirements/benefits. So we can flatten all responsibilities into a single list. Let's list each distinct responsibility as separate
    • . We'll include the capability items as separate
    • as they are duties. Thus Responsibilities list items: - Lead pre-sales and delivery architecture conversations with enterprise customers, owning the technical narrative from discovery through solution design and through implementation oversight. - Design end-to-end Databricks Lakehouse architectures spanning ingestion, transformation, governance, ML, and consumption layers — aligned to Medallion Architecture principles. - Drive EDW modernization engagements, including migration strategies from legacy platforms (Teradata, Netezza, Oracle, SAS, Greenplum, Synapse, Snowflake, on-prem Hadoop) to Databricks, with clear wave planning, risk mitigation, and TCO/ROI articulation. - Advise customers on the adoption of advanced Databricks features