Senior Data Engineering Manager

YipitData is the leading market research and analytics firm for the disruptive economy, seeking a highly skilled Senior Data Engineering Manager to lead their data engineering teams. The role involves developing engineers, guiding technical architecture, and owning critical data pipelines that support products and customer-facing data feeds.

Responsibilities:

• Lead, coach, and develop a global team of data engineers while staying close to architecture, design, code reviews, debugging, and delivery
• Partner with Technical Product Managers and Data leads to translate roadmap priorities, customer needs, and research requirements into scalable technical plans
• Build and improve scalable data pipelines, data models, and QA systems for various data products
• Collaborate with business stakeholders and PMs to support reliable delivery of data pipelines, incident resolution, methodologies, and operational improvements
• Use AI coding tools to develop and enhance methodologies, accelerate engineering execution, improve documentation, strengthen QA, support technical exploration, and raise team productivity
• Create clarity and momentum in ambiguous environments by breaking down complex data, research, and product challenges into actionable engineering plans

Requirements:

• 8+ years of professional experience in data engineering, data architecture, big data development, ETL engineering, or related technical roles
• 3+ years of managerial experience, including mentoring, team leadership, and supporting delivery
• 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
• Experience working with application teams with OLTP and OLAP use cases
• Deep technical judgment across data modeling, distributed data systems, pipeline architecture, orchestration, data quality, observability, and production reliability
• Strong communication and cross-functional collaboration skills, especially with Product, Research, Operations, Client Success, Sales, and Engineering stakeholders
• 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 supporting internal business stakeholders, including collaboration with leadership to aligned on strategic initiatives
• Experience building data pipelines that support AI agents, LLMs, automated insight generation, or AI-powered analytical workflows