Data Engineer

Job Description Weu2019re seeking a highly skilled Data Engineer to design, build, and maintain production-grade data pipelines that process and transform terabytes of data. In this role, youu2019ll collaborate closely with data scientists and other SWEs to ensure that our data infrastructure is scalable, reliable, and cost-effective. Salary Range - $150,000 - $220,000 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 learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: Skills and Requirements -3u20135 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. -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. u2022 Experience with Go programming language. u2003u2022 Familiarity with acceleration frameworks such as RAPIDS or Spark. u2003u2022 Knowledge of cloud platforms (AWS, GCP, Azure). Experience with data version control and MLOps practices.

Created: 2026-03-07