Senior Data Engineering Manager

🕒 July 23

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Logo of YipitData

YipitData

201 - 500 employees

💸 Finance

🏢 Enterprise

Finance • Enterprise • Data Analysis

YipitData is a company that specializes in providing accurate, timely insights on over 1,000 companies by analyzing billions of data points every day. They offer detailed research to help investors make smarter decisions and assist companies in increasing market share, sales, and customer base. YipitData delivers alternative data through a range of datasets, including receipt data, card data, web data, and publicly reported earnings. This data is used to track market trends, consumer behavior, and product categories, enabling businesses to gain transparency into performance metrics. Their services cater to investors, companies, and data partners, with a strong focus on accuracy and near real-time data delivery.

📋 Description

• 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.

🏖️ Benefits

• Flexible work hours • Flexible vacation • Generous 401K match • Parental leave • Team events • Wellness budget • Learning reimbursement • Equity

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