Senior Data Engineering Manager

Join YipitData as a Senior Data Engineering Manager, where you will lead a data engineering team, develop engineers, guide technical architecture, and contribute directly to the systems that support our products and AI platforms. You will own critical central data pipelines and transform complex data into reliable, production-grade assets. This is a hands-on player-coach role that requires strong technical judgment, operational rigor, and people leadership.

• Lead and manage a data engineering team, providing mentorship and guidance to develop engineers and enhance technical skills.
• Oversee the design, development, and maintenance of large-scale data pipelines and production datasets, ensuring accuracy, timeliness, and reliability.
• Collaborate with cross-functional teams to translate roadmap priorities and customer needs into scalable technical plans, driving the successful delivery of data products.
• This role is ideal for an engineering leader who combines strong technical judgment, operational rigor, people leadership, and modern AI-assisted development practices
• You should be comfortable using tools like Claude Code, Codex, Cursor, or similar systems to accelerate implementation, code review, testing, documentation, debugging, and technical exploration while maintaining a high bar for correctness, reliability, and production ownership
• 8+ years of professional experience in data engineering, data architecture, big data development, ETL engineering, or related technical roles
• Experience managing, mentoring, or formally leading data engineers or technical teams in a hands-on player-coach capacity
• Strong hands-on expertise with SQL, PySpark, Databricks, and Airflow or similar workflow orchestration tools and AI toolings
• Experience building, maintaining, or scaling business-critical data systems, including pipelines, production datasets, data delivery systems, or customer-facing data products
• 3+ years of managerial experience, including mentoring, team leadership, and supporting delivery
• Deep technical judgment across data modeling, distributed data systems, pipeline architecture, orchestration, data quality, observability, and production reliability
• Experience working with application teams with OLTP and OLAP use cases
• Strong communication and cross-functional collaboration skills, especially with Product, Research, Operations, Client Success, Sales, and Engineering stakeholders
• Experience supporting internal business stakeholders, including collaboration with leadership to aligned on strategic initiatives
• Experience with alternative data or financial data, including consumer transaction data, email receipt data, B2B spend data, or other large-scale third-party datasets
• Experience building data pipelines that support AI agents, LLMs, automated insight generation, or AI-powered analytical workflows