Software Engineering Manager, Data

About the Role

As Software Engineering Manager, Data, you will lead a 12-person multidisciplinary team spanning data engineering, analytics engineering, data science, machine learning engineering, and data support. Together, these functions own the systems and workflows that move, model, analyze, and operationalize Kard’s data, while supporting both customer-facing product development and the day-to-day needs of our go-to-market teams.

Reporting to the Director of Data & Platform, you will have significant autonomy to shape the team, set technical direction, and determine how we invest across platform development, product integration, automation, and stakeholder support. This is a technical management role: you will not necessarily write production code every day, but you should be able to evaluate architecture, review code and technical designs, guide implementation decisions, and contribute directly when useful.

Responsibilities

• Lead, coach, and develop a multidisciplinary team across data engineering, analytics engineering, data science, machine learning engineering, and data support.
• Set the technical direction for Kard’s data platform, improving its scalability, reliability, maintainability, and usefulness across the organization.
• Increase automation and self-service while reducing manual workflows, recurring operational work, and unnecessary touch points across the Data team.
• Partner with Engineering and Product to integrate data, analytics, and machine learning capabilities into Kard’s products and production systems.
• Ensure Kard’s go-to-market teams have timely access to accurate, actionable data, while turning recurring requests into durable and scalable capabilities.
• Establish clear priorities, ownership, and delivery practices across a portfolio that includes platform investments, product work, analytical needs, and operational support.
• Guide architectural decisions and technical tradeoffs across data pipelines, analytics systems, production models, internal tooling, and data services.
• Build a healthy, accountable team through clear expectations, direct feedback, career development, thoughtful hiring, and strong collaboration across disciplines.

Desired Skills

• Experience managing a technical team working across data engineering, analytics, data science, machine learning, or related software engineering disciplines.
• The technical depth to assess system designs, review code and implementation approaches, and provide credible guidance to experienced engineers and data practitioners.
• Working fluency across modern data platforms, data modeling, pipelines and orchestration, analytics systems, machine learning lifecycles, cloud infrastructure, and production software practices.
• A record of improving technical platforms, automating manual processes, and creating systems that scale with the needs of the business.
• Experience managing a team with a mix of foundational platform work, product development, stakeholder-driven requests, and operational responsibilities.
• Strong judgment about when to build reusable capabilities, when to solve an immediate business need, and how to make those tradeoffs clear to stakeholders.
• The ability to partner effectively with both Engineering and go-to-market teams, translating different needs and constraints into coherent priorities.
• Experience setting expectations, managing performance, supporting career growth, and developing senior technical individual contributors.
• The communication and organizational skills to operate with a high degree of autonomy while maintaining alignment with company and engineering leadership.
• Commitment to fostering an inclusive work environment and actively contributing to team growth and cohesion.
• U.S. core business hours availability and willingness to travel for company meetings.

Bonus Points

• Experience working with fintech, payments, card-linked offers, loyalty, or other transaction-intensive products.
• Experience operating production machine learning systems or integrating models and decisioning capabilities into customer-facing products.
• Experience with high-volume batch and real-time data systems.
• Familiarity with technologies such as Databricks, dbt, AWS, Kubernetes, and Terraform.