Senior Data Engineering Manager
New
Y
YipitDataMarket Research
This role may be performed fully remotely within the United States.Full-TimeManager
SalaryThe annual salary for this position is anticipated to be up to $215,000 per year, with a variable target up to 10%. The compensation package also includes equity.
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Job Details
- Experience
- 8+ years of professional experience in data engineering, data architecture, big data development, ETL engineering, or related technical roles; 3+ years of managerial experience.
- Required Skills
- SQLETLAirflowData engineeringData modelingDatabricksPySpark
Requirements
- 8+ years of professional experience in data engineering, data architecture, big data development, or ETL engineering.
- 3+ years of managerial experience, including mentoring, team leadership, and supporting delivery.
- Hands-on expertise with SQL, PySpark, Databricks, and Airflow.
- Proven experience building, maintaining, or scaling business-critical data systems and production-grade data pipelines.
- Strong technical judgment regarding data modeling, distributed data systems, and pipeline orchestration.
- Deep understanding of data quality frameworks, observability, and production reliability practices.
- Experience working with application teams using both OLTP and OLAP systems.
- Excellent communication and cross-functional collaboration skills.
- Experience using AI-assisted coding tools (e.g., Claude Code, Cursor, Codex) to accelerate development.
Responsibilities
- Lead, coach, and develop a global team of data engineers while maintaining hands-on involvement in architecture, code reviews, and debugging.
- Partner with Technical Product Managers and Data leads to translate roadmaps and customer requirements into scalable technical plans.
- Architect and improve scalable data pipelines, data models, and QA systems to ensure accuracy, reliability, and timeliness.
- Collaborate with internal business stakeholders to support data pipeline delivery and incident resolution.
- Utilize AI coding tools to accelerate engineering execution, improve documentation, and increase team productivity.
- Establish operational excellence through documentation, monitoring, and robust production support frameworks.
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