Senior Data Engineering Manager, YipitData

• 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
• 2+ 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
• Experience building, maintaining, or scaling business-critical data systems, including pipelines, production datasets, data delivery systems, or customer-facing data products
• 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

Desirable

• We are looking for a highly skilled Senior Data Engineering Manager to lead the data engineering team powering our Public Investor business
• This is a hands-on player-coach role for someone who can develop engineers, guide technical architecture, and contribute directly to the systems that support our investment research products and customer-facing data feeds
• You will own critical, customer-facing data systems built on large-scale alternative datasets, including transaction data, email receipt data, B2B spend data, and other third-party datasets
• Your team will transform complex data into reliable, production-grade assets used by research analysts, product teams, internal applications, and external investor clients
• You will lead the data engineering team responsible for building and scaling data systems across YipitData’s Public Investor business, including:
• Large scale data pipelines built for the investor team supporting applications, feeds, and insight agents
• Production datasets and analytical models used in research workflows, internal products, and customer-facing deliverables
• Customer-facing data feeds and recurring external data deliveries with strong expectations around accuracy, timeliness, and reliability
• AI-ready analytical datasets designed with the structure, metadata, documentation, and business context needed for effective use by AI agents and insight-driven applications
• Data quality and observability frameworks, including validation checks, freshness monitoring, coverage monitoring, outlier detection, and automated QA controls
• Technical execution across Databricks, Airflow, SQL, PySpark, and related data infrastructure
• Operational excellence practices across documentation, incident response, monitoring, reliability, and production support
• Lead, coach, and develop a global team of data engineers while staying close to architecture, design, code reviews, debugging, and delivery
• Partner with Product Managers, Application Team, and Research Analysts to translate roadmap priorities, customer needs, and research requirements into scalable technical plans
• Build and improve scalable data pipelines, data models, QA systems, and customer-facing delivery mechanisms for Public Investor data products
• Collaborate with Central Team, Feed Operations, Product Specialists, Client Success, and GTM teams to support reliable delivery, incident resolution, client communication workflows, and operational improvements
• Use AI coding tools to 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