Senior Data Engineering Manager
New
Based in the United StatesFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- 8+ years of experience in data engineering, data architecture, or large-scale data systems development
- Required Skills
- LeadershipSQLAirflowData engineeringData modelingDatabricksPySpark
Requirements
- 8+ years of experience in data engineering, data architecture, or large-scale data systems development.
- 2+ years of experience in a managerial or technical leadership role within data engineering teams.
- Strong hands-on expertise with SQL, PySpark, Databricks, and Airflow or equivalent data processing and orchestration tools.
- Proven experience building and scaling production-grade data pipelines, datasets, or data products.
- Deep understanding of distributed data systems, data modeling, pipeline architecture, and data quality best practices.
- Experience delivering customer-facing data products or external data feeds with high reliability expectations.
- Strong leadership, mentoring, and stakeholder management skills across technical and non-technical teams.
- Ability to operate effectively in fast-paced, ambiguous environments with strong ownership and execution focus.
- Strong communication skills with experience collaborating across product, research, operations, and engineering functions.
Responsibilities
- Lead and develop a global data engineering team, providing hands-on technical guidance while overseeing architecture, design, and delivery of large-scale data systems.
- Own end-to-end data pipelines, production datasets, and analytical models supporting investment research workflows and customer-facing data products.
- Build and maintain scalable, reliable data infrastructure using tools such as Databricks, Airflow, SQL, and PySpark.
- Oversee the development of customer-facing data feeds, ensuring accuracy, timeliness, reliability, and consistency across delivery channels.
- Design and implement data quality frameworks, including validation checks, monitoring systems, anomaly detection, and automated QA processes.
- Collaborate cross-functionally with product, research, engineering, client success, and operations teams to align data solutions with business needs.
- Drive operational excellence across incident management, observability, documentation, and production support.
- Use AI-assisted development tools to accelerate engineering output, improve code quality, and enhance team productivity.
- Translate complex data and business requirements into clear technical execution plans and scalable architectures.
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