Databricks Architect/Engineer

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 co's 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 - 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.Requirements : - Strong hands-on Databricks Architect/Databricks Engineer Profile with end-to-end build-and-solution capability and Databricks professional certification.- Must have 6+ years in Data engineering/Data architect roles, with at least recent 3 years of hands-on Databricks experience.- Must be able to design a solution end-to-end and then build it themselves.- Must have experience translating client/business requirements into Databricks solution designs - data pipelines, Lakehouse layouts, and serving layers.- Must have built and maintained end-to-end pipelines - ingestion (Auto Loader, DLT), transformation (PySpark/SQL or dbt), and serving (Unity Catalog, Databricks SQL).- 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.- 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.- Must have strong SQL and PySpark skills, able to read and reason about existing pipelines quickly.- 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. (ref:hirist.tech)