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.

#LI-SS1

You will be successful in this role if you have:

• 12+ years of experience in data architecture, data engineering, or enterprise analytics — with at least 5 years focused on Databricks platform delivery.

• Databricks Certified Data Engineer Professional or Databricks Certified Associate Developer for Apache Spark — required. Additional Databricks certifications strongly preferred.
• Expert-level proficiency in Apache Spark (PySpark, Scala Spark) — performance tuning, DAG optimization, shuffle management, and broadcast strategies.
• Deep hands-on expertise with Delta Lake: ACID transactions, Z-ordering, OPTIMIZE, VACUUM, time travel, and schema enforcement/evolution.
• Proven production experience with Delta Live Tables (DLT): expectations, quarantine patterns, SCD Type 1/2 in DLT, and pipeline monitoring.
• Strong Unity Catalog implementation experience: catalog/schema/table hierarchy design, privilege inheritance, service principals, and attribute-based access control.
• Experience architecting Databricks on at least one major cloud: Azure Databricks (ADLS Gen2, Azure AD), AWS (S3, IAM Instance Profiles, Glue Catalog), or GCP (GCS, Dataproc comparison).
• Proficiency in infrastructure-as-code for Databricks: Terraform (databricks provider), Databricks Asset Bundles (DABs), and CI/CD integration (GitHub Actions, Azure DevOps, Jenkins).
• Hands-on MLflow experience: experiment tracking, model registry, model serving, and custom pyfunc models.
• Strong SQL expertise for Databricks SQL / Photon query optimization, materialized views, and lakehouse serving patterns.
• Experience with real-time streaming architectures: Kafka + Databricks Structured Streaming, Autoloader, and watermarking strategies.

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