Senior Data Engineering Manager

YipitData

7h•Remote

About The Position

YipitData is seeking a highly skilled Senior Data Engineering Manager to lead the data engineering team for its Public Investor business. This is a hands-on player-coach role responsible for developing engineers, guiding technical architecture, and contributing directly to systems supporting investment research products and customer-facing data feeds. The role involves owning critical, customer-facing data systems built on large-scale alternative datasets, transforming complex data into reliable, production-grade assets for various stakeholders. The ideal candidate combines strong technical judgment, operational rigor, people leadership, and modern AI-assisted development practices, comfortable using AI coding tools to enhance productivity and quality.

Requirements

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.

Nice To Haves

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 customer-facing data feeds, including APIs, flat files, cloud storage, portal-based delivery, or recurring feed delivery systems.
Experience building data pipelines that support AI agents, LLMs, automated insight generation, or AI-powered analytical workflows.

Responsibilities

Lead the data engineering team responsible for building and scaling data systems across YipitData’s Public Investor business.
Manage large scale data pipelines built for the investor team supporting applications, feeds, and insight agents.
Oversee production datasets and analytical models used in research workflows, internal products, and customer-facing deliverables.
Ensure the reliability and accuracy of customer-facing data feeds and recurring external data deliveries.
Develop AI-ready analytical datasets with appropriate structure, metadata, documentation, and business context for AI agents and applications.
Implement and maintain data quality and observability frameworks, including validation checks, freshness monitoring, coverage monitoring, outlier detection, and automated QA controls.
Drive technical execution across Databricks, Airflow, SQL, PySpark, and related data infrastructure.
Champion operational excellence practices including 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 Teams, and Research Analysts to translate roadmap priorities into scalable technical plans.
Build and improve scalable data pipelines, data models, QA systems, and customer-facing delivery mechanisms.
Collaborate with various teams (Central Team, Feed Operations, Product Specialists, Client Success, GTM) to ensure reliable delivery and operational improvements.
Utilize AI coding tools to accelerate engineering execution, improve documentation, strengthen QA, and raise team productivity.
Create clarity and momentum in ambiguous environments by breaking down complex challenges into actionable engineering plans.