Software Engineer Python

Mountain View, CA - USA

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Role : Software Engineer

Location : Mountain View CA (5 days working)

Minimum Basic Requirements

Python Proficiency: Write update and maintain Python frameworks and libraries to support data processing and integration tasks.
Composer / Apache Airflow: Hands-on experience with Apache Airflow including updating DAGs managing multi-node rollouts and troubleshooting issues
Code Management: Use Git and GitHub for source control code reviews and version management.
GCP Proficiency: Extensive experience working with GCP services (e.g. Big Query Cloud Dataflow Pub/Sub Cloud Storage Monitoring). Knowledge of resource labeling and automation through SDKs or APIs.
Software Engineering: Strong understanding of software engineering best practices including version control (Git) collaborative development (GitHub) code reviews and CI/CD.
Problem-Solving: Excellent problem-solving skills with the ability to tackle complex data engineering challenges.
Communication: Excellent stakeholder communication skills with the ability to interface directly with data scientists platform engineers and other clients to explain complex technical details coordinate rollouts triage issues and provide updates
Bachelors or masters degree in computer science Engineering Computer Information Systems Mathematics Physics or a related field or software development training program

What youll do

Develop and enhance Python frameworks and libraries to support cost tracking data processing data quality lineage governance and MLOps.
Implement data processing optimizations to reduce the cost of our larger training data and features pipelines.
Build scalable features and training data batch pipelines leveraging Big query Dataflow and Composer scheduler/executor framework on Google Cloud Platform.
Implement monitoring logging and alerting systems to ensure the reliability and stability of our data and ml infrastructure and pipelines.
Plan and oversee infrastructure rollouts including phased deployments validation and rollback strategies.
Act as primary point of contact for Data Scientists ML Engineers and other stakeholders handling rollout coordination communications and issue resolution.
Collaborate with ML Platform engineers to ensure seamless integration of updates into workflows.
Document processes and changes providing clear runbooks and handoffs for ongoing support.

Preferred Requirements

Python Mastery: Strong Python development background with demonstrated experience maintaining and extending production-grade SDKs or internal libraries
Change Management: Experience with infrastructure rollout and change management including phased deployments validation and rollback strategies
Resiliency: Comfortable working in fast-moving ML/AI platform environments where reliability transparency and client experience are key
Batch Pipelines: Experience with building deploying and maintaining production batch pipelines processing and publishing petabytes of data.

Role : Software Engineer Location : Mountain View CA (5 days working) Minimum Basic Requirements Python Proficiency: Write update and maintain Python frameworks and libraries to support data processing and integration tasks. Composer / Apache Airflow: Hands-on experience with Apache Airflow i...

Role : Software Engineer

Location : Mountain View CA (5 days working)

Minimum Basic Requirements

Python Proficiency: Write update and maintain Python frameworks and libraries to support data processing and integration tasks.
Composer / Apache Airflow: Hands-on experience with Apache Airflow including updating DAGs managing multi-node rollouts and troubleshooting issues
Code Management: Use Git and GitHub for source control code reviews and version management.
GCP Proficiency: Extensive experience working with GCP services (e.g. Big Query Cloud Dataflow Pub/Sub Cloud Storage Monitoring). Knowledge of resource labeling and automation through SDKs or APIs.
Software Engineering: Strong understanding of software engineering best practices including version control (Git) collaborative development (GitHub) code reviews and CI/CD.
Problem-Solving: Excellent problem-solving skills with the ability to tackle complex data engineering challenges.
Communication: Excellent stakeholder communication skills with the ability to interface directly with data scientists platform engineers and other clients to explain complex technical details coordinate rollouts triage issues and provide updates
Bachelors or masters degree in computer science Engineering Computer Information Systems Mathematics Physics or a related field or software development training program

What youll do

Develop and enhance Python frameworks and libraries to support cost tracking data processing data quality lineage governance and MLOps.
Implement data processing optimizations to reduce the cost of our larger training data and features pipelines.
Build scalable features and training data batch pipelines leveraging Big query Dataflow and Composer scheduler/executor framework on Google Cloud Platform.
Implement monitoring logging and alerting systems to ensure the reliability and stability of our data and ml infrastructure and pipelines.
Plan and oversee infrastructure rollouts including phased deployments validation and rollback strategies.
Act as primary point of contact for Data Scientists ML Engineers and other stakeholders handling rollout coordination communications and issue resolution.
Collaborate with ML Platform engineers to ensure seamless integration of updates into workflows.
Document processes and changes providing clear runbooks and handoffs for ongoing support.

Preferred Requirements

Python Mastery: Strong Python development background with demonstrated experience maintaining and extending production-grade SDKs or internal libraries
Change Management: Experience with infrastructure rollout and change management including phased deployments validation and rollback strategies
Resiliency: Comfortable working in fast-moving ML/AI platform environments where reliability transparency and client experience are key
Batch Pipelines: Experience with building deploying and maintaining production batch pipelines processing and publishing petabytes of data.