Solutions Architect - Data Engineering

FourLeaf Federal Credit Union • Town of Oyster Bay, NY

21h • $117,456 - $146,819 • Remote

About The Position

The Solutions Architect – Data Engineering serves as the principal technical architect and hands-on engineer responsible for designing, building, and scaling FourLeaf'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.

Requirements

• Bachelors Degree in Computer Science, Information Technology, or related field.
• 15+ years of progressive experience in data engineering, data architecture, database development, and enterprise analytics with experience in solution architecture and technical leadership roles with demonstrated ownership of enterprise-scale data platforms.
• Hands-on experience with cloud data platforms (Snowflake required; additional cloud DW experience valued).
• Experience with modern ETL/ELT tools, with Matillion expertise strongly preferred.
• SnowPro Advanced: Architect and SnowPro Advanced: Data Engineer
• Matillion Associate or Professional Certification
• Databricks Certified Data Engineer Professional or Databricks Certified Machine Learning Professional
• AWS Certified Data Analytics or Solutions Architect
• Snowflake ecosystem (SnowSQL, SnowPipe, and Snowflake Data Sharing)
• SQL
• Python
• ETL tools
• Data Visualization tools (Power BI, Sigma, and or Tableau)
• cloud platforms (AWS, Azure, or Google Cloud).
• Microsoft Office (Excel, Word, PowerPoint, Teams, Outlook).

Nice To Haves

• Masters degree in Computer Science, Information Technology, or related field.
• Additional certifications are considered a plus

Responsibilities

• 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.
• 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.
• 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.
• 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.
• Cost Governance: Implement Snowflake cost‑management strategies including monitoring, warehouse optimization, and chargeback/showback models.
• Performance Optimization: Optimize queries, warehouse sizing, and resource utilization to balance performance and cost.
• Usage Analytics: Analyze consumption trends and identify optimization opportunities.
• Budget Management: Partner with finance and IT to forecast and manage Snowflake spend.
• Standards Enforcement: Define and enforce development practices supporting cost‑efficient usage.
• AI‑Assisted Engineering: Use AI tools to accelerate pipeline development, code generation, and documentation.
• Snowflake Cortex Integration: Leverage Cortex capabilities for data processing and enrichment.
• Automation Strategy: Identify repetitive engineering tasks suitable for AI‑driven automation.
• Innovation Leadership: Stay current on AI advancements relevant to data engineering and evaluate adoption opportunities.
• Semantic Architecture: Build and maintain a semantic layer with consistent business definitions, metrics, and data products.
• Member 360: Develop comprehensive member‑centric data products that enable insights into behavior, engagement, and opportunities.
• Standardized Metrics: Deliver enterprise KPIs for Lending, Deposits, Digital, Branch Performance, and more.
• Regulatory Analytics: Support NCUA, BSA/AML, GLBA, and other compliance requirements through curated data products.
• BI Integration: Optimize models for consumption in Power BI and Sigma Computing.
• Predictive Analytics Infrastructure: Build the data infrastructure and feature stores required for predictive modeling (risk, churn, behavior, growth).
• Prescriptive Analytics: Enable next‑best‑action and prescriptive decision‑support pipelines.
• MLOps Collaboration: Work with data scientists to operationalize models and ensure reliable data flows and monitoring.
• Analytics Sandbox: Maintain secure, well‑structured environments for experimentation and advanced analytics.

Benefits

• medical, dental, and vision coverage
• life and disability insurance
• voluntary benefit programs
• a 401(k) plan with employer match
• reimbursement and wellness programs
• an annual performance-based bonus

What This Job Offers

• Job Type: Full-time
• Career Level: Senior