What success looks like in this role:
Databricks Platform Architecture
- Architect enterprise Databricks Lakehouse platforms on Azure, AWS, or GCP — including Unity Catalog, Delta Live Tables (DLT), and Databricks SQL.
- Design multi-workspace, multi-region Databricks deployments with workspace federation, network isolation (Private Link / V Net injection), and identity federation via Azure AD / Okta / SCIM.
- Define medallion architecture (Bronze / Silver / Gold) standards, enforcing schema evolution, data contracts, and SLA-tiered pipeline SLOs.
- Lead migration of legacy data warehouses (Teradata, Netezza, Snowflake, Synapse) to the Databricks Lakehouse — including SQL translation, workload profiling, and TCO modeling.
- Own cluster architecture decisions: autoscaling policies, instance fleet configurations, job vs. interactive cluster strategies, Photon engine enablement, and cost-per-query optimization.
- Design and implement Databricks Workflows and Delta Live Tables for mission-critical streaming and batch pipelines, including CDC (Change Data Capture) patterns using Autoloader and Structured Streaming.
Data Governance & Security
- Implement Unity Catalog as the enterprise meta store: fine-grained access control (row/column-level security), lineage tracking, and cross-workspace catalog federation.
- Define data classification frameworks, PII masking strategies, and dynamic views for regulatory compliance (GDPR, HIPAA, SOX) within the Databricks platform.
- Architect Delta Sharing for secure, governed cross-organizational data sharing without data movement.
- Lead data mesh and data product design patterns on Databricks — aligning ownership, SLAs, and discoverability across domains.
AI & ML Platform (Databricks ML)
- Design end-to-end ML platforms using Databricks ML Runtime, ML flow (Tracking, Registry, Projects), and Feature Store for model lifecycle management.
- Architect LLM / Generative AI workloads on Databricks: RAG pipelines, fine-tuning with Mosaic AI, vector search (Databricks Vector Search), and Model Serving endpoints.
- Define MLOps frameworks covering CI/CD for models, drift detection, A/B testing, and shadow deployments using ML flow and Databricks Workflows.
- Integrate Databricks AI/BI (Genie, Dashboards) for self-service analytics and natural language data querying.
Stakeholder & Delivery Leadership
- Lead architecture reviews, technical workshops, and proof-of-concept engagements with C-suite and VP-level client stakeholders.
- Author solution architecture documents, reference architectures, and RFP/RFI responses for Databricks-led pursuits.
- Define center-of-excellence (CoE) standards, reusable accelerators, and Databricks best-practice playbooks for the Unisys Data & AI practice.
- Mentor and upskill a team of data architects and engineers; drive Databricks certification paths across the practice.
- Partner with Databricks field engineering on joint go-to-market opportunities and co-delivery engagements.
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You will be successful in this role if you have:
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