Engineering Manager, Data Transformation
Leads and develops the Data Transformation engineering team, driving scalable data infrastructure, materialized datasets, and the team’s technical roadmap. Requires engineering management experience with data pipelines and distributed systems, plus strong cross-functional leadership.
About the job
Responsibilities
- Deliver reliable, efficient infrastructure and services that scale to users' needs.
- Lead, mentor, and support a team of engineers.
- Partner with infrastructure, product engineering, and high-visibility stakeholder teams on critical initiatives.
- Understand user needs and pain points to prioritize engineering work and deliver high-quality solutions.
- Drive projects through planning, development, and delivery while maintaining quality and timeliness.
- Provide hands-on technical leadership across architecture, design, vision, requirements, and incident response.
- Define and execute the long-term Data Transformation roadmap.
- Foster a collaborative, inclusive environment that supports innovation, knowledge sharing, and continuous improvement.
- Partner with recruiting to attract talent and define team hiring strategies.
Requirements
- 3+ years of experience managing teams that have shipped and operated data pipelines and critical distributed-system infrastructure.
- Experience recruiting and building high-performing teams.
- Strong customer focus and commitment to partnerships with other engineers.
- Effective cross-functional collaboration, rigorous thinking, clear communication, and sound decision-making.
- Ability to operate autonomously and responsibly in ambiguous environments.
- Technical acumen to guide architecture and strategic technical decisions with staff engineers.
- Commitment to a healthy, inclusive, challenging, and supportive work environment.
Nice-to-haves
- Experience managing teams that shipped products to data users and operated large-scale, highly available data transformation pipelines.
- Expertise in Kafka, Flink, Spark, Airflow, Python, SQL, and API design.
- Enjoyment of learning system details and directing architectural decisions.
- Strong written and verbal communication skills for technical and non-technical audiences.
Skills
Data Pipelines, Distributed Systems, Kafka, Flink, Spark, Airflow, Python, SQL, API Design, Incident Response
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