Senior Data Architect

Career Opportunities with BSI Financial Services

We're building a modern data platform to power our mortgage lending and servicing platform from the ground up. As our Senior Data Architect, you'll be the technical visionary and hands‑on builder who defines our data platform strategy and leads its execution.

This is a player‑coach role where you'll architect solutions, write code, and lead a small engineering team (2‑3 people) while partnering closely with our business‑focused Senior Manager of Data Products & Analytics.

You're not just drawing architecture diagrams. You're building the platform, driving modernization, and making it real.

• Modernizing from legacy (SQL Server, .NET) to modern stack (Databricks, dbt, Sigma)
• Building 7 AI‑powered product categories (borrower intelligence, investor analytics, employee copilots, document intelligence, compliance monitoring, automated decisioning, predictive analytics)
• Creating a data lakehouse that powers analytics and AI/ML products

• Be THE technical leader who makes this transformation happen
• Design the architecture, then build it yourself (with your small team)
• Drive adoption of modern data capabilities across the organization
• Partner with business leaders to deliver products that matter

• Define the technical vision and roadmap:
• Create the modernization blueprint for our legacy A360 application (aging .NET app ? modern UI with embedded analytics)
• Design our lakehouse architecture on Databricks (medallion layers, data modeling, governance)
• Define the AI product architecture (how do 7 product categories integrate and share intelligence?)
• Support build vs buy vs partner decisions on technology
• Establish data platform standards, patterns, governance and best practices
• Design and build data pipelines (Databricks, dbt)
• Develop APIs and backend services for A360 modernization
• Build proof‑of‑concepts for AI products (LLM integrations, ML models)
• Write production‑quality code (Python, SQL, dbt)
• Debug complex technical problems when your team is stuck
• Optimize performance (query tuning, pipeline efficiency, cost optimization)
• Team is small (2‑3 engineers) - you need to code alongside them
• Complex problems need senior technical expertise
• We're building, not maintaining - can't just delegate
• Credibility comes from doing:
  Here’s how I’d architect this... let me show you
• Examples of What You’ll Build:
• dbt transformation pipelines for mortgage loan data
• Document classification and extraction engine (OCR + LLM)
• APIs for A360 modernized frontend
• Productionize AI and ML models
• Lead a small, focused engineering team:
• Manage 2‑3 data engineers and 1 DBA
• Set technical priorities and remove blockers
• Code reviews and technical mentorship
• Develop team capabilities (upskill on modern stack)
• Balance team development with hands‑on contribution
• Drive adoption of the platform you're building:
• Run Lakehouse Office Hours (teach business users how to leverage the platform)
• Create demos and proof‑of‑concepts to show what's possible
• Present to executives and business leaders (sell the vision)
• Train analysts and business users on self‑service analytics (Sigma)
• Build internal advocates and champions
• Own data platform strategy and architecture (Databricks lakehouse, data modeling, governance) including operational databases.
• Build and optimize data pipelines
• Establish data quality framework (monitoring, alerting, anomaly detection)
• Define security and compliance architecture (data access, audit trails, encryption)
• A360 Modernization (High Priority)
• Create technical blueprint for modernizing legacy A360 application
• Design API layer (modern frontend ? backend data services)
• Architecture for embedded analytics (Sigma widgets in A360 UI, LLM integration)
• Migration strategy (phased approach, parallel systems, SQL Server ? Databricks)
• Coordinate with application developers on frontend development

• Lead 2‑3 data engineers in building platform capabilities
• Take over DBA responsibilities across operational databases
• Mentor team on modern data stack (Databricks, dbt, cloud architecture)
• Code reviews and technical guidance
• Build team culture of quality and ownership