Google Cloud Platform/ETL Data Engineer - only local
Quick Overview
Job Description
Maxonic maintains a close and long-term relationship with our direct client. In support of their needs, we are looking for a Data Engineer
Job Title: Data Engineer
Job Type: Fulltime
Job Location: Santa Clara, CA
Work Schedule: Hybrid 4 days onsite
Description:
We’re 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, you’ll collaborate closely with data scientists and other SWEs to ensure that our data infrastructure is scalable, reliable, and cost-effective.
Must have
- data pipeline design / data engineer
- automating pipelines
- streamline processes and automation
- model generation should be automated
- building data pipelines specifically for his team
- more pipeline design . wants to stream line. ETL - like.
- MUST HAVE **airflow on kubernetes
- distributed training in a kubernetes cluster with N nodes
- ingestion, transform - knows ETL
- what does versioning mean
- how do you do drift detection
- IMPORTANT **how do you schedule jobs using airflow??
- data set size - multi terabytes of data. high transactions, but always on a schedule
Responsibilities
- Pipeline Development & Deployment:
- Architect, develop, and deploy batch and streaming pipelines using Airflow and containerized workflows for cyber-security use-cases.
- Containerize data-processing jobs with Docker, orchestrate with Kubernetes, and manage releases with Helm charts.
- Distributed Computing:
- Build high-throughput data transformations using Dask or Apache Spark.
- Maintain training data clusters across hybrid (on-prem and cloud environments).
- Optimize training jobs for performance, resiliency, and cost.
- Monitoring & Reliability:
- Implement observability (logging, metrics, alerting) to maintain pipeline health and SLA adherence.
- Troubleshoot, debug, and resolve data-processing failures in production.
- 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 data into cloud storage and lakes, ensuring performance and accessibility.
Qualifications:
- 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.
- Google Cloud Platform 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.
About Maxonic:
Since 2002 Maxonic has been at the forefront of connecting candidate strengths to client challenges. Our award winning, dedicated team of recruiting professionals are specialized by technology, are great listeners, and will seek to find a position that meets the long-term career needs of our candidates. We take pride in the over 10,000 candidates that we have placed, and the repeat business that we earn from our satisfied clients.
Interested in Applying?
Please apply with your most current resume. Feel free to contact Pramod Kumar (/) for more details.
Skills
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