Jobs and Careers

Director, Data Engineering

United Statesfull_timeVerifiedPosted 6 Mar 2025

About the role

We provide technology that community financial institutions use to manage risk and drive growth. Our solutions automate key processes – from anti-money laundering to fraud detection to CECL readiness to lending workflows – empowering our customers by addressing their Enterprise Risk Management needs. We are seeking a Director of Data Engineering to lead and scale our data and AI engineering organization. This role will be instrumental in defining the technical strategy and architecture for a highly scalable, cloud-based data platform that powers our financial technology solutions, including financial transaction analysis and other mission-critical products. This leader will be responsible for designing and implementing best-in-class data infrastructure, pipelines, and engineering practices, ensuring high availability, security, and performance for financial institutions. They will build and manage high-performing teams, drive data platform innovation, and collaborate closely with cross-functional stakeholders to support business and product objectives. This position will be based in Raleigh, NC, Austin, TX, or remote. Occasional travel is required for team events and strategic collaboration. What You'll Do: Data Platform & Technical Strategy

Define and execute a scalable, resilient data platform strategy that aligns with business objectives and industry best practices.
Architect and drive the adoption of cloud-native data solutions (AWS, Azure, GCP), including data lakes, data warehouses, and real-time streaming pipelines.
Lead initiatives around data governance, security, and compliance (SOC 2, PCI DSS), ensuring financial industry standards are met.
Drive automation-first approaches with Infrastructure-as-Code (IaC), self-service data platforms, and DevOps methodologies.
Stay ahead of emerging data engineering trends, including serverless computing, AI/ML integration, ML Ops patterns and practices, and event-driven architectures.

Leadership & Team Development

Build and mentor a high-performing data and AI platform engineering team, fostering a culture of innovation, collaboration, and continuous learning.
Scale and structure the engineering team to support the growth of distributed data-intensive applications.
Provide strong leadership for engineering managers, setting clear objectives, roadmaps, and career development pathways.
Promote a culture of psychological safety, technical excellence, and accountability.

Execution & Operational Excellence

Establish best-in-class data engineering and DevOps practices, including CI/CD pipelines, observability, and automated testing.
Optimize data ingestion, transformation, and processing for high-performance analytics and machine learning applications.
Define and track key performance indicators (data availability, pipeline latency, SLA adherence) to drive continuous improvement.
Oversee Agile/Scrum processes, ensuring efficient and predictable delivery of data platform initiatives.

Cross-Functional Collaboration

Partner with Product and Engineering teams to build scalable data solutions that enable advanced analytics, credit/lending models, and financial insights.
Work closely with Finance, Compliance, and Risk teams to ensure data integrity, security, and adherence to financial regulations.
Collaborate with Executive Leadership to communicate data strategy, platform evolution, and business impact.

What You'll Need:

Bachelor’s degree in Computer Science, Engineering, or a related technical field (Master’s degree or MBA a plus).
10+ years of software engineering experience, with at least 5 years in senior leadership roles managing engineering managers and cross-functional teams.
Deep experience in cloud-based data architectures (AWS, Azure, GCP), with expertise in data lakehouses, data streaming (Kafka, Kinesis), and distributed databases.
Hands-on experience building high-scale, real-time data processing pipelines, utilizing Spark, Flink, or similar frameworks.
Strong background in data security, governance, and compliance, particularly in financial services.
Proficiency in Kubernetes, Docker, and microservices architectures for scalable data processing.