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Data Engineer (Remote

Remote / Online - Candidates ideally in
Calgary, Alberta, D3J, Canada
Listing for: Socket.dev
Remote/Work from Home position
Listed on 2026-08-03
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Data Engineering, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Position: Data Engineer (Remote)
About The Company
Crowd Strike is a global leader in cybersecurity dedicated to stopping breaches with an advanced AI-native platform. Renowned for its innovative approach, Crowd Strike leverages cutting-edge technology to provide comprehensive security solutions that protect organizations from sophisticated cyber threats. With a focus on innovation, integrity, and customer-centricity, the company empowers businesses worldwide to defend their digital assets effectively. Crowd Strike's commitment to excellence and its dynamic work environment make it an ideal place for professionals seeking to make a meaningful impact in the cybersecurity landscape.

About

The Role
We are seeking a highly skilled Principal Data Engineer with deep expertise in Large Language Models (LLMs) and AI platforms to join our team. In this strategic role, you will be responsible for designing, building, and deploying robust data infrastructure that underpins our next-generation AI-driven security products. Your work will involve developing scalable, fault-tolerant, and cost-effective data solutions at exabyte scale, enabling Crowd Strike to stay at the forefront of cybersecurity innovation.

You will provide technical leadership across teams, mentor engineers, and collaborate closely with research and product teams to transform prototypes into production-grade services. This is an exceptional opportunity for a seasoned data engineering professional to influence the future of AI-powered cybersecurity solutions and lead complex projects that have a global impact.

Qualifications

Master's or PhD in Computer Science, Data Engineering, or a related STEM field, or equivalent practical experience.

10+ years of progressive experience in data engineering or platform engineering, with at least 3 years focused on AI/ML or data science platforms at large scale.

Hands-on experience with LLM engineering, including fine-tuning, prompt engineering, and deployment.

Strong expertise in Retrieval-Augmented Generation (RAG) and agentic workflows.

Proven track record in designing and delivering large-scale distributed systems, including sharding, partitioning, and concurrency management.

Exceptional coding skills in high-level programming languages such as Python and JVM technologies, with a focus on performance, maintainability, and testing.

Deep understanding of engineering best practices, including code reviews, resilient architecture, and comprehensive testing strategies.

Experience in leading engineering teams, providing mentorship, and conducting technical workshops and reviews.

Familiarity with MLOps tools (MLflow, Sage Maker, Vertex AI), containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP, OCI).

Knowledge of distributed data processing frameworks like Spark, Dask, and Flink, as well as data warehousing solutions such as Snowflake and Big Query.

Experience with message queuing and streaming technologies like Kafka and Pulsar.

Responsibilities

Architect, implement, and optimize data platforms and pipelines for LLMs, RAG, and AI agentic systems at exabyte scale.

Drive the adoption and deployment of agentic workflows and techniques to create autonomous, data-driven security features.

Design scalable, fault-tolerant, and cost-effective data solutions that support rapid iteration and high-quality deployment.

Write production-ready code emphasizing performance, maintainability, and rigorous testing to ensure reliable delivery.

Provide technical leadership in data modeling, normalization, and semantic cataloging for AI/ML workloads.

Establish and promote best practices for MLOps and Data Ops, including monitoring, observability, and zero-touch recovery mechanisms.

Mentor engineering teams through workshops, design reviews, and technical guidance to strengthen AI platform capabilities.

Collaborate with research and product teams to transition prototypes into scalable, production-grade services.

Manage the entire lifecycle of critical data services from development through deployment and ongoing monitoring.

Benefits

Market-leading compensation and equity awards.

Comprehensive physical and mental wellness programs.

Competitive vacation and holiday…
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