Databricks Solution Architect

About the Role

We are building our modern data platform on Databricks and need a Solution Architect to own the end-to-end design. You will turn business needs from Risk, Fraud, Finance, Marketing, Collections and Product into scalable, secure data architectures that integrate cleanly with existing systems. You will own architectural decisions across teams and work closely with the Principal Engineer, who turns the architecture into detailed designs and working pipelines.

Key Responsibilities

Target-state architecture: Define the enterprise data architecture on Databricks: ingestion, medallion layering, serving and ML, and how they connect to source systems, the legacy warehouse, loan servicing platforms and vendor feeds.
Discovery and roadmaps: Run discovery with business and technology stakeholders and turn requirements into solution designs, reference architectures and delivery roadmaps.
Marketing architecture: Design for identity resolution, segmentation, campaign and attribution measurement, and compliant data sharing with marketing platforms and vendors.
Collections architecture: Design for near-real-time delinquency and contact data, agency and vendor file exchange, and compliant handling of contact preferences and regulatory restrictions.
Cross-cutting decisions: Decide on batch vs treaming, build vs. buy, workspace topology, network and multi-account design, and disaster recovery.
Security architecture: Design the security and compliance architecture with InfoSec: Unity Catalog access model, PII/NPI protection, encryption, audit logging and lineage.
Migration planning: Plan and sequence the migration of legacy Hadoop/Hive/EMR and warehouse workloads, including cutover strategy and risk mitigation.
Cost and business case: Build the cost and capacity model (DBU forecasting, FinOps guardrails) and the business case for platform investments.
Governance: Chair the architecture review board and maintain standards, reference patterns and decision records.
Vendors: Manage the relationship with the Databricks account team and cloud provider, and lead vendor evaluations and proofs of concept.

Must-Have Skills (candidate must meet all)

Experience: 12+ years in data engineering or architecture, with 5+ years on Databricks lakehouse platform.
Data platform fundamentals: Strong Spark, Delta Lake and SQL, plus data modeling (medallion, dimensional).
AWS architecture: Networking, IAM, encryption and multi-account design on AWS.
Secure design: Platform design for PII/NPI, including Unity Catalog access models and lineage.
Migration: Migration planning from legacy Hadoop/Hive/warehouse systems.
Streaming and batch design: Ability to choose between batch and streaming (Kafka, Structured Streaming) and design for both.
Resilience: Disaster recovery and high-availability design.
Cost modeling: FinOps and Databricks cost and capacity modeling, including a business case.
Communication: Stakeholder management, vendor management and clear architecture documentation.
Good-to-Have Skills (preferred, not required)

Banking, lending or other regulated financial services experience
Marketing data architecture (customer 360, consent, campaign measurement)
Collections data architecture (delinquency, agency and dialer feeds)
ML/MLOps architecture on Databricks (feature store, MLflow)
Databricks and AWS Solution Architect certifications