Solutions Architect - Data Engineering

Job Category: Enterprise and Data Analytics Office

Requisition Number: SOLUT
006901

• Posted :
  September 21, 2026
• Full-Time

Showing 1 location

Bethpage, NY 11714, USA

It Starts With Our Employees

Four Leaf's ultimate goal is to provide the best-in-class member and employee experience, and it all begins with two things: hiring incredible people and giving them a great place to work.

What You’ll Do

The Solutions Architect – Data Engineering serves as the principal technical architect and hands‑on engineer responsible for designing, building, and scaling Four Leaf's enterprise data platform. This role bridges strategic vision with technical execution, translating business requirements into robust, scalable data solutions that power analytics, AI, and operational decision-making across the organization.

This position is open to flexible work options including a remote set up.

Core Contributions

• End‑to‑End Architecture Ownership: Design and maintain an enterprise data platform that provides a unified, governed view of credit union operations across all domains.
• Medallion Architecture: Implement Bronze/Silver/Gold layer strategy with data contracts, transformation standards, and quality expectations.
• Domain Data Modeling: Create and maintain enterprise data models for Member, Deposit, Transaction, Lending, Finance, Marketing, Digital, and Regulatory domains.
• Governance & Standards: Define architectural patterns, modeling standards, naming conventions, and documentation practices.
• Reference Architecture: Develop and maintain reference architectures and reusable design patterns for the broader EDAO team.
• Data Ingestion & Integration
  • Pipeline Strategy & Implementation: Lead the design of ingestion pipelines for 75+ systems of record including digital platforms, payment processors, lending systems, and third‑party providers.
  • Matillion Framework Development:
    • Build reusable ETL/ELT patterns, templates, and parameterized components.
    • Standardize batch, near‑real‑time, and streaming ingestion methods.
    • Implement error handling, retry logic, and alerting frameworks.
  • Integration Patterns: Define approaches for database connections, APIs, file transfers, and CDC‑based ingestion.
  • Quality at Source: Embed validation, profiling, and quality checks within ingestion workflows.
• Solution Architecture & Hands‑On Engineering
  • End-to-End Project Delivery: Lead mid‑to‑large data initiatives from requirements through deployment.
  • Technical Solution Design: Translate complex business needs into technical specifications, data models, and implementation plans.
  • Hands‑On Delivery: Build critical pipelines, transformations, and data products while setting engineering quality standards.
  • Technical Problem‑Solving: Serve as the expert problem solver for performance challenges, integration issues, and architectural decisions.
  • Stakeholder Collaboration
    :
    Work directly with business analysts, product owners, and subject matter experts to understand requirements and deliver solutions that meet business needs.
• Data Ops & Observability
  • End‑to‑End Observability: Design frameworks for monitoring data quality, freshness, pipeline reliability, and lineage.
  • Monitoring and Alerting: Implement proactive alerts and incident‑response processes.
  • SLA & Reliability Management: Define, measure, and optimize SLAs for critical business processes.
  • Operational Excellence: Introduce automated testing, CI/CD pipelines, and environment management.
  • Incident Management: Develop runbooks and lead root‑cause analysis for operational issues.
• Snowflake Fin Ops & Cost Optimization
  • Cost Governance: Implement Snowflake cost‑management strategies including monitoring, warehouse optimization, and chargeback/showback models.
  • Performance Optimization: Optimize queries, warehouse…