Director of Data Engineering

Hinge is the dating app designed to be deleted
In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With tens of millions of users across the globe, we’ve become the most trusted way to find a relationship, for all.
About the Role
Hinge is seeking an experienced Director of Data Engineering to lead and advance our Data Engineering discipline, primarily supporting our Product organization. Data Engineering plays a critical role in building and maintaining the data pipelines and data products that power Hinge’s analytics, experimentation, and machine learning capabilities. Your work will enable teams across the company to leverage high-quality, trusted data to drive informed decision-making, ultimately helping users find meaningful connections.
As the Director of Data Engineering, you will be a core member of the Data Leadership Team, responsible for contributing to our overall data strategy while setting the strategic direction for the Data Engineering team. You will collaborate with leaders across the organization to understand their data needs and translate these needs into scalable and efficient solutions. You’ll drive innovation in data architecture and foster a culture of operational excellence. 
Hinge is embarking on an AI-driven future, and this role is critical in enabling that journey with data. Your leadership will be instrumental in shaping the future of Hinge’s data ecosystem, enabling the company to grow and evolve while maintaining the highest standards of quality, reliability, accessibility, and performance around our data.

Responsibilities

• Play a key role on the Data Leadership Team, shaping Hinge’s overall data strategy while setting and executing the Data Engineering strategy. 
• Ensure strategic alignment with Hinge’s broader business, product, and technology objectives, driving data-driven innovation across the company.
• Enable Hinge’s AI and machine learning initiatives by building and optimizing high-quality, scalable, and reliable data pipelines. 
• Transform the current existing data engineering practices into unified and streamlined standards that enable optimized, high quality, scalable, and reliable data pipelines.
• Lead, scale, and mentor a high-performing team of data engineers and data engineering managers, fostering a culture of growth, collaboration, innovation, and operational excellence.
• Drive technical excellence, upholding high standards for code quality, system design, scalability, and reliability across all data engineering initiatives.
• Provide hands-on technical leadership, actively participating in architecture reviews, design discussions, and guiding critical technical decisions.
• Collaborate
•  cross-functionally with BI Engineers, Backend and Client Engineers, ML Engineers, Data Scientists, and Product Managers to develop and implement scalable, data-driven solutions that power analytics, experimentation, and machine learning.
• Ensure seamless data integration across Hinge’s products, features, and systems, enabling a unified, high-quality data ecosystem.
• Define and implement best practices that enhance team efficiency, optimize workflows, and drive continuous improvement in data engineering operations.
• Stay ahead of industry trends and emerging technologies, driving innovation in data engineering to support Hinge’s evolving business needs.

What We're Looking For

• 10+ years of experience in Data Engineering or a related field, with at least 4 years in engineering management leading high-performing teams.
• Demonstrated expertise in developing and maintaining scalable, reliable, and high-quality data products and pipelines that support business-critical analytics, experimentation, and AI/ML workloads.
• Experienced at building and managing data pipelines that hydrate online ML features.
• Deep expertise with modern data engineering technologies, including Kafka, Spark, dbt, Airflow, Databricks, and cloud-based data platforms (AWS, GCP, or Azure).
• Strong familiarity with modern cloud-native architectures and infrastructure, ensuring efficient, secure, and cost-effective data solutions.
• Strategic technical leadership with a proven ability to align data engineering strategies with product and business objectives, driving initiatives that exceed business goals.
• Passion for developing, coaching, and scaling high-performing teams, fostering a culture of growth, inclusivity, and operational excellence.
• Exceptional communication and collaboration skills, wit