Data Principal Engineer for BlueCat's Intelligent NetOps products. Building data lakes and maintaining data services with cross-functional collaboration.
Responsibilities
You will be responsible for building our data lake, maintaining our data pipelines / services and facilitating the movement of messages and implementing data analytics jobs.
Work directly with the Application teams and Platform teams to enable growth of the BlueCat data-driven products.
Design, develop, and operate highly reliable large-scale data lake.
Partner closely with Applications teams to understand requirements and influence the Data strategy by providing expert advice on design and architecture.
Make critical decisions regarding technical directions, scalability, and system performance.
Collaborate with cross-functional teams to create innovative solutions.
Optimize network configurations to ensure low-latency communication and high availability.
Implement data protection mechanisms to safeguard critical data processed from the edge to the core.
Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
10+ years of hands-on experience designing and building large-scale distributed data platforms, with deep expertise in OLAP databases, query engines, data lakes, object storage, distributed file systems, and analytics infrastructure.
Proven expert-level experience with ClickHouse architecture, performance optimization, cluster design, data modeling, ingestion pipelines, storage engines, replication, partitioning, and large-scale analytical workloads.
Strong experience building modern data lake architectures around ClickHouse, including integration with open table formats such as Apache Iceberg, Parquet, and Delta Lake.
Excellent software engineering and coding skills with production experience in Python (including async programming), Golang, and C/C++.
Deep understanding of high-performance data processing, vectorized query execution, parallel/distributed compute frameworks, and low-latency analytical systems.
Experience with modern data processing and analytics frameworks such as Pandas, Polars, DuckDB, Dask, Ray, Prefect, Bodo, or equivalent technologies.
Passion for designing and building scalable, high-quality REST APIs and distributed services, while mentoring other engineers in API and platform design best practices.
Strong expertise in infrastructure optimization, performance engineering, scalability, reliability, and operational excellence, with the ability to drive measurable improvements in latency, throughput, and availability.
Strong understanding of cloud-native distributed systems, including Kubernetes, containers, CI/CD, observability, infrastructure automation, and production operations.
Experience deploying and operating large-scale data platforms in AWS; experience with GCP and Azure is considered a plus.
Strong understanding of networking and distributed systems fundamentals, including TCP/IP, DNS, HTTP/S, service discovery, and secure service-to-service communication.
Demonstrated engineering rigor with a strong focus on software architecture, testing strategies, security, privacy, resiliency, and operational ownership.
Ability to independently lead architecture, design, development, testing, deployment, and production operations of complex distributed data systems from day one.
Strong communication and leadership skills, with the ability to collaborate effectively across engineering, product, architecture, and executive stakeholders.
Experience influencing technical direction, mentoring senior engineers, and acting as a technical leader for strategic platform initiatives.
Highly desirable: Kubernetes certifications such as CKAD, CKA, or equivalent cloud-native platform experience.
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