Databricks Architect/Databricks Engineer

KEY 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
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 strong 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

Work arrangement
No