KEY RESPONSIBILITIES

- 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

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

2Mandatory (Experience 1): Must have 5+ years in Data engineering/Data architect roles, with at least recent 3 years of hands-on Databricks experience

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

4Mandatory (Experience 3):



Current-role project work must clearly align with the JD — the resume must describe the current project (what it is, the candidate's own scope, and the Databricks components used), not just list skills. Generic or JD-mirrored bullets without a concrete project will not be considered

5Mandatory (Experience 4): Must have experience translating client/business requirements into Databricks solution designs — data pipelines, Lakehouse layouts, and serving layers

6Mandatory (Experience 5): Must have built and maintained end-to-end pipelines — ingestion (Auto Loader, DLT), transformation (PySpark/SQL or dbt), and serving (Unity Catalog, Databricks SQL)

7Mandatory (Certification): Must hold at least one active Databricks Professional-level certification (Data Engineer Professional preferred)

8Mandatory (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

9Mandatory (Tech skill 2): Must have robust SQL and PySpark skills, able to read and reason about existing pipelines quickly

10Mandatory (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.

11Mandatory (Company) - Must come from an IT services/consulting background with direct delivery on client engagements, US or global clients preferred

12Mandatory (Stability) - Must show stable tenure: 2+ years average per employer, and no unexplained career gaps

13Mandatory (Note 1) - Must be currently working hands-on on Databricks in their present role, not on an adjacent platform with past Databricks experience

14Mandatory (Note 2): CTC is inclusive of 20% variable

15Mandatory (Note 3) : Role is Hybrid, WFH flexibility as well upto 6 days a month

16Preferred (Domain): Pharma or life sciences background

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