Senior Data Engineering Manager

Our data and research teams transform raw data into strategic intelligence, delivering accurate, timely, and deeply contextualized analysis that our customers—ranging from the world’s top investment funds to Fortune 500 companies—depend on to drive high-stakes decisions. From sourcing and licensing novel datasets to...

We operate globally with offices in the US, APAC, and India. Our award-winning, people-centric culture—recognized by Inc. as a Best Workplace for three consecutive years—emphasizes transparency, ownership, and continuous mastery.

YipitData isn’t a place for coasting—it’s a launchpad for ambitious, impact-driven professionals.

We are looking for a highly skilled Senior Data Engineering Manager to lead one of our data engineering teams. 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 products, AI platforms, and customer-facing data feeds.
You will own critical central data pipelines 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, and internal applications.
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.
You will lead the data engineering team responsible for building and scaling data systems for all of YipitData’s businesses, including:
Large scale data pipelines built for the public investor business units, corporate investor team, and/or private investor team
Production datasets and analytical models used in research workflows, applications,  internal products, and customer-facing deliverables.
Architecting data flows and data models to support various business stakeholders use cases focusing on accuracy, timeliness, and reliability.
AI-ready analytical datasets designed with the structure, metadata, documentation, and business context needed for effective use by AI agents..
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 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.

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.
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.