Data Engineer

We’re seeking a highly skilled Data Engineer to design, build, and maintain production-grade data pipelines that process and transform terabytes of data. This role involves close collaboration with data scientists and software engineers to ensure data infrastructure is scalable, reliable, and cost-effective.

Pipeline Development & Deployment - Architect, develop, and deploy batch and streaming pipelines using Airflow and containerized workflows for cybersecurity use cases.
Containerize data-processing jobs with Docker, orchestrate with Kubernetes, and manage releases using Helm charts.

Distributed Computing - Build high-throughput data transformations using Dask or Apache Spark.
Maintain training data clusters across hybrid environments (on-prem and cloud).
Optimize training jobs for performance, resiliency, and cost efficiency.

Monitoring & Reliability - Implement observability solutions (logging, metrics, alerting) to maintain pipeline health and SLA adherence.
Troubleshoot, debug, and resolve data-processing failures in production environments.

Collaboration & Best Practices - Work with cross-functional teams to define data contracts, schemas, and quality checks.
Enforce software engineering best practices: CI/CD, code reviews, automated testing, and documentation.

Data Modeling & Storage - Design and maintain data models and schemas for AI/ML continuous training use cases.
Load and manage data in cloud storage and data lakes, ensuring performance and accessibility.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

- 3–5 years of professional experience designing and operating production data pipelines at scale.
- Containerization & Orchestration: Expertise with Docker, Kubernetes, and Helm.
- Workflow Management: Hands-on experience building DAG-based pipelines in Apache Airflow.
- Programming: Strong proficiency in Python for data engineering tasks.
- Distributed Frameworks: Practical experience with - Dask or Apache Spark for large-scale data processing.
- Cloud Fundamentals: Familiarity with deploying and managing services in a cloud environment.
- Compiled Languages: Experience writing data services in Go or Rust.
- GCP Proficiency: Hands-on with Google Cloud services (e.g., Pub/Sub, Big Query, Cloud Storage, GKE). Equivalent
experience in other public cloud providers is fine.
- ML Pipelines: Exposure to deploying cross-cluster model-training workflows using Ray or similar frameworks.
- Infrastructure as Code: Familiarity with Terraform for deployment.
- Security & Compliance: Knowledge of data governance, encryption, and role-based access control.

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.