Databricks Architect/Databricks Engineer

Roles & Responsibilities

KEY RESPONSIBILITIES

1. Solutioning & Architecture

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  Translate client requirements into Databricks-based solution designs - data pipelines, Lakehouse layouts, and serving layers.

  
  


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  Recommend the right Databricks components (Delta Live Tables, Workflows, Unity Catalog, Databricks SQL, Genie) for a given use case, based on data volume, latency, and governance needs.

  
  


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  Participate in pre-sales and proposal discussions, contributing effort estimates and technical approach for Databricks-based engagements.

  
  


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  Review architecture decisions with senior architects and flag risks or better alternatives early.

  
  

2. Hands-On Build & Delivery

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  Build and maintain end-to-end pipelines: ingestion (Auto Loader, DLT), transformation (dbt or native PySpark/SQL), and serving (Unity Catalog, Databricks SQL).

  
  


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  Work directly with pharma commercial datasets - IQVIA, Symphony, CRM, Hub/SP, claims - modeling them into clean, governed Delta Lake structures.

  
  


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  Develop and maintain reusable components: notebooks, job templates, SQL libraries, and data quality checks.

  
  


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  Configure and tune Genie Spaces and AI/BI dashboards for client-facing analytics use cases.

  
  


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  Own workspace-level hygiene: cluster policies, job scheduling, cost tracking, and basic performance tuning.

  
  

3. Platform Currency & Best Practices

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  Stay closely tracked with new Databricks releases and features (e.g. Lakehouse//RT, Genie enhancements, Metric Views) and assess their relevance to pharma use cases.

  
  


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  Bring new capabilities into existing client engagements where they create real value, not just for novelty.

  
  


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  Contribute to and maintain DataZymes' internal Databricks standards, templates, and knowledge base.

  
  


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  Support the certification and upskilling of junior engineers and analysts on the team.

  
  

4. Client & Team Collaboration

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  Act as the day-to-day Databricks technical point of contact on assigned client engagements.

  
  


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  Explain technical trade-offs in plain terms to non-technical stakeholders when needed.

  
  


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  Collaborate with analytics, forecasting, and delivery teams to make sure the platform serves the actual business question, not just the data movement.

  
  

5. Practice Building

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  Help establish the Databricks practice at DataZymes - codifying reusable design patterns, reference architectures, and coding standards as the team's project count grows.

  
  


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  Design and build solution accelerators for common pharma use cases (prescription analytics, patient cohort analysis, omnichannel attribution) that can be reused and adapted across clients.

  
  


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  Maintain the internal Databricks knowledge base - templates, checklists, and lessons learned from delivery.

  
  


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  Support partnership conversations with Databricks by contributing technical input - solution briefs, architecture references, and demo material - that the practice lead and account teams can take into partner and client discussions.

  
  


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  Help identify gaps in team capability and contribute to certification and enablement plans for engineers joining the practice.