About the Role We're looking for a hands-on Databricks engineer to design and build end-to-end Lakehouse solutions for pharma commercial data — from solutioning and architecture through pipeline development, client-facing analytics, and practice building. Key Responsibilities

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

Recommend the right Databricks components (DLT, Workflows, Unity Catalog, Databricks SQL, Genie) based on data volume, latency, and governance needs

Contribute to pre-sales/proposal discussions with effort estimates and technical approach

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

Work with pharma commercial datasets (IQVIA, Symphony, CRM, Hub/SP, claims), modeling them into governed Delta Lake structures

Develop reusable components — notebooks, job templates, SQL libraries, and data quality checks

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

Own workspace hygiene — cluster policies, job scheduling, cost tracking, and performance tuning

Track new Databricks releases and assess relevance to pharma use cases

Contribute to internal Databricks standards, templates, and knowledge base; support junior engineer upskilling

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





Explain technical trade-offs in plain terms to non-technical stakeholders

Help build the Databricks practice — reusable design patterns, reference architectures, and solution accelerators for pharma use cases (prescription analytics, patient cohort analysis, omnichannel attribution)

Support partnership conversations with Databricks through solution briefs and demo material Requirements

6–9 years in data engineering or analytics platform roles, with 3+ years hands-on Databricks experience

Solid working knowledge of Delta Lake, Delta Live Tables, Unity Catalog, Auto Loader, Databricks SQL, Workflows, and cluster/job configuration

Comfortable designing solutions end-to-end and building them yourself — not a hand-off-to-engineering role

At least one active Databricks Professional-level certification (Data Engineer Professional preferred)

Strong SQL and PySpark skills; able to read and reason about existing pipelines quickly

Genuine interest in staying current with Databricks releases and evaluating what's actually helpful

Comfortable working at both delivery and practice level — building for clients while contributing to reusable standards Good to Have

Pharma or life sciences analytics background (IQVIA, Symphony Health, Hub/SP, CRM, or claims data)

Experience configuring Genie Spaces or AI/BI dashboards for business users

Exposure to dbt, Fivetran, or similar orchestration tools alongside Databricks

Prior experience at a pharma analytics or consulting firm

📌 Databricks Architect/Databricks Engineer (Bengaluru)
🏢 DataZymes
📍 Bengaluru