Roles & Responsibilities :

1. Solutioning & Architecture :

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

- 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.

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

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

2. Hands-On Build & Delivery :

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

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

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

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

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

3. Platform Currency & Best Practices :

- 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.

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

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

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

4. Client & Team Collaboration :

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

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

- 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 :

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

- 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.

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

- 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.

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

Ideal Candidate :

- 1. Strong hands-on Databricks Architect/Databricks Engineer Profile with end-to-end build-and-solution capability and Databricks professional certification.

- 2. Mandatory (Experience 1): Must have 6+ years in Data engineering/Data architect roles, with at least recent 3 years of hands-on Databricks experience.

- 3. Mandatory (Experience 2): Must be able to design a solution end-to-end and then build it themselves.

- 4. Mandatory (Experience 3): Must have experience translating client/business requirements into Databricks solution designs - data pipelines, Lakehouse layouts, and serving layers.

- 5. Mandatory (Experience 4): Must have built and maintained end-to-end pipelines - ingestion (Auto Loader, DLT), transformation (PySpark/SQL or dbt), and serving (Unity Catalog, Databricks SQL).

- 6. Mandatory (Certification): Must hold at least one active Databricks Professional-level certification (Data Engineer Professional preferred), OR deep hands-on project experience with an Associate certification in hand.

- 7. Mandatory (Tech skill 1): Must have solid working knowledge across the Databricks stack - Delta Lake, Delta Live Tables, Unity Catalog, Auto Loader, Databricks SQL, Workflows, and cluster/job configuration.

- 8. Mandatory (Tech skill 2): Must have strong SQL and PySpark skills, able to read and reason about existing pipelines quickly.

- 9. Mandatory (Communication): Must be able to act as the client-facing Databricks technical point of contact and explain technical trade-offs in plain terms to non-technical stakeholders.