Data Architect - Data Engineering & Business Intelligence

Position Overview: We are seeking an experienced Data Architect - Data Engineering & Business Intelligence to lead and influence the design, governance, and evolution of our enterprise data platform. This role is responsible for defining the architectural vision across Data Engineering, Business Intelligence, Analytics, Metadata Management, Data Governance, and AI-ready data platforms. The successful candidate will establish enterprise architecture standards, drive technology strategy, and design scalable, secure, and high-performing data solutions that power enterprise reporting, advanced analytics, machine learning, and future AI initiatives. The ideal candidate will have deep expertise architecting enterprise-scale data platforms on AWS, with hands-on experience in Amazon Redshift, Amazon S3 Tables (Apache Iceberg), Amazon EMR, Apache Spark, and modern data warehousing technologies. Experience with Snowflake and/or Databricks is highly desirable. Working closely with Product Engineering, Business Intelligence, Analytics, Finance, Collections, and Portfolio Management Analytics teams, the Data Architect will translate business requirements into scalable, reliable, and high-performing data solutions while driving engineering excellence across the organization. What You'll Architect: - Enterprise Data Warehouse and Modern Lakehouse Platform - AWS-based Enterprise Analytics Platform - Apache Iceberg Lakehouse on Amazon S3 Tables - Enterprise Metadata Management and Data Governance - Enterprise Semantic Layer and KPI Framework - Scalable Data Platforms for Business Intelligence, Advanced Analytics, and AI - Enterprise Data Quality and Observability Frameworks Key Responsibilities: Enterprise Data Architecture: - Define and own enterprise data architecture strategy. - Design conceptual, logical and physical data models. - Design dimensional (Star/Snowflake) and Data Vault models. - Establish enterprise architecture standards and reusable design patterns. Data Platform Engineering: - Architect enterprise ETL/ELT solutions. - Define standards for ingestion, transformation, orchestration and storage. - Lead adoption of Apache Iceberg on Amazon S3 Tables. - Optimize platform performance, scalability and reliability. Metadata, Governance & Data Quality: - Define enterprise metadata strategy. - Establish standards for Data Catalog, Business Glossary, Lineage, KPI Definitions and Data Quality. Architecture Governance: - Lead architecture reviews and establish engineering standards. - Review SQL, Python, Spark and ETL implementations. Technology Strategy & Innovation: - Define technology roadmap and evaluate emerging technologies. Leadership & Collaboration: - Partner with Product Engineering, BI, Analytics, Finance, Collections and Portfolio Management Analytics teams. - Mentor engineers and promote engineering excellence. Required Technical Skills: - Cloud Data Platforms: Amazon Redshift, Amazon S3, Amazon S3 Tables (Apache Iceberg), Amazon EMR, Amazon Athena (preferred). Snowflake/Databricks desirable. - Data Architecture & Modeling: Enterprise Data Modeling, Dimensional Modeling, Data Vault, Semantic Layer, MDM, KPI & Metric Modeling. - Data Platform Engineering: Apache Spark (PySpark), Python, Advanced SQL, ETL/ELT, Batch & Streaming Processing, Pipeline Design, Performance Tuning. - Data Integration: Apache Kafka, AWS DMS, REST APIs, CDC. - Metadata & Governance: Apache Atlas (preferred), Metadata Management, Data Catalog, Business Glossary, Data Lineage, Data Stewardship, Data Quality. - Business Intelligence: Tableau, Semantic Layer, KPI Frameworks, Data Mart Design, Self-Service Analytics. - Engineering Practices: Git, Jenkins, Terraform, CI/CD, IaC, Code Reviews. Required Qualifications: Experience: - 10+ years of relevant experience designing, building and governing enterprise data platforms. - Experience defining architecture standards and implementing cloud data platforms. - Experience as Data Architect, Lead Data Engineer, Principal Data Engineer or similar technical leadership role. - Strong mentoring and stakeholder management skills. Education: - Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, Mathematics or related discipline (or equivalent experience). Preferred Qualifications: - Experience with Apache Atlas, DataHub, Collibra or Alation. - Experience with data quality and observability frameworks. - Experience building AI-ready data platforms. - Financial Services/FinTech experience preferred. - AWS, Snowflake or Databricks certifications preferred. What Success Looks Like: Within the first 12 months: - Establish enterprise architecture standards. - Modernize the Lakehouse using Apache Iceberg on Amazon S3 Tables. - Standardize metadata, semantic layer and KPI definitions. - Improve platform scalability, reliability and cost efficiency. - Strengthen engineering quality and mentoring. - Enable an AI-ready enterprise data platform.