Enterprise Architect - Databricks

Enterprise Architect - Databricks

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.

Unisys is proud to be an equal opportunity employer that considers all qualified applicants without regard to age, blood type, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law.

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Experience Level

Senior Level