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 recent 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.

📌 Databricks Architect/Databricks Engineer (Bengaluru)
🏢 Important Company of the Sector
📍 Bengaluru