Senior Databricks Engineer (ML or Analytics)

We need to output HTML fragment with sections: About the role? The posting includes description. We need to use

for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must not repeat job title as heading. No Job ID. Remove boilerplate. Include sections only when posting has real specific content. We need to parse content: The posting includes: - Intro: BlueFlag is hiring Senior Databricks Engineers... description of role, focus areas, what you'll do. - "What You'll Do" bullet list (actually sentences). We'll treat as Responsibilities. - "AI / ML focus" and "Analytics focus" subparts under What You'll Do? Actually they are part of What You'll Do. Could be separate sections? Might be better to have Responsibilities covering all. - "Why Join BlueFlag" maybe About the role or Benefits? Could be About the role (company description). Could be a section "About the role" describing role and company. - "Requirements" section with bullet list. - Then "AI / ML focus" and "Analytics focus" under Requirements? Actually after Requirements there are sub-sections: AI / ML focus and Analytics focus with specific experience. Those are additional qualifications/skills. - Then "Desired Databricks certifications..." etc. Those are also qualifications/skills. - Then "Experience with healthcare data..." etc. - Then "Benefits" section with bullet list. - No explicit Pay or Schedule. Thus we will create sections:

About the role

with paragraph summarizing role description (intro + Why Join BlueFlag maybe). Must preserve all substantive info; we can combine.

Responsibilities

with

• items from What You'll Do and focus areas.

  Requirements

  with
  • items from Requirements list.

    Qualifications

    maybe for AI/ML focus and Analytics focus specifics? Or we could put those under Requirements as sub-items. But spec says use headings for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can have Qualifications for the focus-specific items and desired certifications etc. Skills maybe for technical skills list? But we already have many skills in Requirements. Could separate: Requirements = years, degree, citizenship; Qualifications = specific experience; Skills = technical proficiencies; Benefits = benefits. But we must not create sections with filler. Must have real content. Let's extract: Intro paragraph: "BlueFlag is hiring Senior Databricks Engineers to help Department of Veterans Affairs teams move their data work to a premier data and artificial intelligence (AI) platform built on Databricks in Azure Commercial. The role is hands-on and customer-facing, much like professional services. You will work alongside VA teams to understand their on-premises workloads, rebuild them on Databricks, and teach them to use the platform with confidence. We're hiring for two focus areas. The ML focus helps teams build, deploy, and monitor machine learning models. The Analytics focus helps teams move ETL pipelines and reporting to Databricks. Both need the same foundation: a data engineer who has built production pipelines and enjoys helping customers succeed. You'll join a friendly, collaborative team doing work that makes a real difference for Veterans and their families." Why Join BlueFlag paragraph: "At BlueFlag, we're passionate about leveraging cutting-edge technology to make a real difference. You'll be at the forefront of cloud innovation, working on projects that directly impact people's lives. We offer a high-growth, entrepreneurial environment that values fresh ideas and authentic teamwork. If you're ready to take your data engineering career to new heights and contribute to meaningful projects that push the boundaries of technology, we want to hear from you. Join BlueFlag and be part of a team that's shaping the future of cloud solutions!" We can combine into About the role. Responsibilities: From "What You'll Do" bullet list (though not actual bullet but sentences). Also AI/ML focus and Analytics focus specifics. Let's list responsibilities: - Work directly with VA teams to scope use cases, plan migrations, and deliver working solutions on Databricks - Assess on-premises workloads (SQL Server, SSIS packages, stored procedures, scheduled jobs) and adapt them to Databricks patterns in Azure Commercial - Build and tune pipelines with PySpark, Spark SQL, Delta Lake, Lakeflow, and Azure Data Factory - Use Unity Catalog for access, lineage, and sharing, so customer data stays governed as it moves - Teach customers through workshops, pairing, and clear documentation so they can own their workloads - Package reusable templates and code (Git, CI/CD, Databricks Asset Bundles) that speed up the next migration AI/ML focus responsibilities: - Build end-to-end ML workflows: feature engineering, training, evaluation, MLflow tracking, the Unity Catalog model registry, and model serving - Help data scientists move models from local notebooks and on-premises servers into governed, monitored production - Support generative AI use cases, such as retrieval-augmented generation (RAG) and agents, alongside predictive models Analytics focus responsibilities: - Convert SSIS and stored procedure ETL into Databricks pipelines, with reconciliation that proves the results match - Replace on-premises reporting (SSRS reports, SSAS cubes) with Databricks SQL, AI/BI dashboards, and Power BI - Publish reporting data from Databricks SQL warehouses, including semantic models and query performance tuning Requirements (bullet list): - 7+ years in data engineering, including 2+ years building production workloads on Databricks - Strong proficiency in Python, SQL, PySpark, and Spark SQL - Hands-on experience with Delta Lake, lakehouse (medallion) design, and Spark performance tuning - Hands-on Unity Catalog experience: catalogs, grants, lineage, and row- and column-level security - Experience migrating on-premises workloads (e.g., SQL Server, SSIS, stored procedures) to the cloud - Experience with Azure services, including ADLS Gen2, Azure Data Factory, Entra ID, and Key Vault - Experience with Git, CI/CD, and infrastructure as code (e.g., Databricks Asset Bundles, Terraform) - Customer-facing consulting or professional services experience, including requirements sessions, workshops, and training - Clear written and verbal communication with technical and non-technical audiences - Bachelor's degree in computer science, information systems, or a related field - US Citizen: Must be a citizen of the United States - Security Clearance: Must be able to obtain a public trust clearance. Must be eligible to work in the United States AI/ML focus qualifications (under Requirements? but we can put under Qualifications): - Experience training, evaluating, and deploying models with MLflow and common frameworks (e.g., scikit-learn, XGBoost, PyTorch) Analytics focus qualifications: - Experience with Databricks SQL and Power BI data modeling (star schemas, semantic models, DAX) - Desired Databricks certifications (Data Engineer Professional; Machine Learning Professional or Data Analyst Associate by focus) - Experience with healthcare data (HL7, FHIR, OMOP) and HIPAA requirements for PHI - Experience in Azure Government or other FedRAMP High environments - Experience upgrading Hive metastore workloads to Unity Catalog (e.g., UCX) - AI/ML focus: Mosaic AI Vector Search, Agent Framework, or model monitoring - Analytics focus: Microsoft Fabric, Pyramid Analytics, or Genie Benefits: - Competitive salary - Generous annual leave and paid holidays - Comprehensive group health and dental plans - 401(k) with company match - Life insurance and AD&D coverage - Ongoing training and professional development opportunities No Pay or Schedule specifics. Thus sections: About the role, Responsibilities, Requirements, Qualifications, Benefits. We could also have Skills but we already covered technical skills in Requirements; but we could create Skills section for proficiencies like Python, SQL, etc. However that would duplicate content. Better to keep Requirements as is and maybe Qualifications for focus-specific and desired certifications etc. We must not create empty sections. Now produce HTML fragment: use

    for each heading, then

    for prose,

    • for lists. We need to preserve all substantive information; do not invent or summarize away detail. So we must keep each bullet exactly as in text. We must not include boilerplate like cookie/navigation/legal/EEO etc. Not present. Now craft HTML. We'll need to escape any HTML special characters? Not needed. Let's write:

      About the role

      BlueFlag is hiring Senior Databricks Engineers to help Department of Veterans Affairs teams move their data work to a premier data and artificial intelligence (AI) platform built on Databricks in Azure Commercial. The role is hands-on and customer-facing, much like professional services. You will work alongside VA teams to understand their on-premises workloads, rebuild them on Databricks, and teach them to use the platform with confidence. We're hiring for two focus areas. The ML focus helps teams build, deploy, and monitor machine learning models. The Analytics focus helps teams move ETL pipelines and reporting to Databricks. Both need the same foundation: a data engineer who has built production pipelines and enjoys helping customers succeed. You'll join a friendly, collaborative team doing work that makes a real difference for Veterans and their families.

      At BlueFlag, we're passionate about leveraging cutting-edge technology to make a real difference. You'll be at the forefront of cloud innovation, working on projects that directly impact people's lives. We offer a high-growth, entrepreneurial environment that values fresh ideas and authentic teamwork. If you're ready to take your data engineering career to new heights and contribute to meaningful projects that push the boundaries of technology, we want to hear from you. Join BlueFlag and be part of a team that's shaping the future of cloud solutions!

      Then Responsibilities:

      Responsibilities

      • Work directly with VA teams to scope use cases, plan migrations, and deliver working solutions on Databricks
      • Assess on-premises workloads (SQL Server, SSIS packages, stored procedures, scheduled jobs) and adapt them to Databricks patterns in Azure Commercial
      • Build and tune pipelines with PySpark, Spark SQL, Delta Lake, Lakeflow, and Azure Data Factory
      • Use Unity Catalog for access, lineage, and sharing, so customer data stays governed as it moves
      • Teach customers through workshops, pairing, and clear documentation so they can own their workloads
      • Package reusable templates and code (Git, CI/CD, Databricks Asset Bundles) that speed up the next migration
      • AI/ML focus: Build end-to-end ML workflows: feature engineering, training, evaluation, MLflow tracking, the Unity Catalog model registry, and model serving
      • AI/ML focus: Help data scientists move models from local notebooks and on-premises servers into governed, monitored production
      • AI/ML focus: Support generative AI use cases, such as retrieval-augmented generation (RAG) and agents, alongside predictive models
      • Analytics focus: Convert SSIS and stored procedure ETL into Databricks pipelines, with reconciliation that proves the results match
      • Analytics focus: Replace on-premises reporting (SSRS reports, SSAS cubes) with Databricks SQL, AI/BI dashboards, and Power BI
      • Analytics focus: Publish reporting data from Databricks SQL warehouses, including semantic models and query performance tuning
      Requirements:

      Requirements

      • 7+ years in data engineering, including 2+ years building production workloads on Databricks
      • Strong proficiency in Python, SQL, PySpark, and Spark SQL