Principal Software Engineer, Data Infrastructure
Remote in the US, with a preference for candidates in the New York City areaFull-TimePrincipal
Salary198000 - 220000 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSPythonGCPJavaKafkaKubeflowKubernetesAirflowGoSparkdbt
Requirements
- 10+ years of software engineering experience with a focus on distributed systems, data platforms, and ML infrastructure
- Proven ability to influence technical direction across multiple teams and mentor senior/staff engineers
- Proven expertise in data processing frameworks and table formats (e.g. Spark, Flink, Iceberg)
- Proven expertise in orchestration tools (e.g. Airflow, Kubeflow)
- Deep knowledge of ML infrastructure: model training pipelines, feature stores, registries, serving, and monitoring
- Strong programming skills in Python
- Strong programming skills in at least one compiled language like Java or Go
- Experience designing systems with scalability, reliability, and cost-efficiency as first-class concerns
- Cloud platform experience (AWS, GCP)
- Familiarity with Kubernetes and modern data platform architectures
- Familiarity with compliance and governance in data/ML systems (auditability, privacy, explainability)
- Familiarity with the data lakehouse paradigm and medallion architecture
Responsibilities
- Design and evolve infrastructure for data ingestion, storage, batch and streaming pipelines, and machine learning workflows
- Build systems for training, deploying, monitoring, and governing models, including feature stores, registries, and inference platforms
- Ensure end-to-end system reliability, monitoring, and cost transparency across data and ML workloads
- Deliver frameworks and APIs that enable engineers, analysts, and ML scientists to build and operate solutions independently
- Evaluate and introduce emerging technologies (vector databases, distributed training, orchestration frameworks, LLM stacks) and establish adoption guidelines
- Partner with platform, product, and engineering and ML science leaders to align on strategy and accelerate delivery
- Guide senior and staff engineers, lead architecture reviews, and raise the technical bar across data and ML domains
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