Staff Data Engineer, Platform Engineering
New
United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSSparkCI/CDData modelingDebugging
Requirements
- 10+ years of experience in data engineering, distributed systems, or software engineering roles
- Proven track record designing and operating production-grade data platforms at scale
- Strong expertise in distributed processing frameworks such as Spark or equivalent
- Solid software engineering fundamentals including system design, testing, CI/CD, and debugging
- Experience working in cloud environments (AWS preferred), including storage, compute, and security architectures
- Deep understanding of data modeling, pipeline design, and data quality enforcement
- Experience building multi-tenant systems with strong isolation and reliability requirements
- Ability to evaluate complex technical tradeoffs and make durable architectural decisions
- Strong communication skills and experience leading through both design and execution
Responsibilities
- Lead the design, development, and evolution of core data platform systems, ensuring scalability, reliability, and performance across complex distributed environments
- Architect multi-tenant, multi-cloud data systems and define lifecycle patterns spanning ingestion, transformation, validation, enrichment, and serving
- Contribute directly to production systems while setting engineering standards for quality and operability
- Lead architecture and development of core platform capabilities, including processing frameworks, storage systems, and shared data services
- Design and operate large-scale distributed data systems across batch and streaming workloads
- Establish data quality, validation, and governance frameworks to ensure consistency and auditability
- Define and evolve data lifecycle patterns across ingestion, transformation, and delivery layers
- Integrate platform systems with CI/CD pipelines, observability tooling, and infrastructure automation frameworks
- Make architectural tradeoffs across scalability, cost, reliability, and performance
- Drive ambiguous, high-impact technical initiatives from definition through execution
- Contribute to production code and setting standards for engineering excellence and system reliability
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