About The Role:

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.

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.

What Youll Own

You will lead the data engineering team responsible for building and scaling data systems across YipitDatas 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.

What Youll Do

• 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.

What Were Looking For

• 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 Have

• 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.

What We Offer:

Our compensation package includes comprehensive benefits, perks, and a competitive salary

• We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
• Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!

The annual salary for this position is $200,000, with a variable target bonus of 10%. The compensation package also includes equity. The final offer may be determined by a number of factors, including, but not limited to, the applicants experience, knowledge, skills, abilities, as well as internal team benchmarks.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have office hubs in Austin, Miami, and Mountain View. If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.