Vehicle Propulsion Systems Engineering

This position places strong emphasis on Google Cloud Platform (GCP), especially Tekton pipelines, Cloud Storage buckets, and Cloud Functions. The engineer will help design event-driven data workflows, automate deployment and testing, and implement cloud security controls as part of daily delivery. The role bridges data engineering and analytics, transforming and operationalizing data so teams can consume trusted insights faster.

1. Build and maintain analytics data pipelines and transformation workflows for powertrain datasets.
2. Design and operate GCP-native solutions using Cloud Storage, Cloud Functions, Pub/Sub, and BigQuery.
3. Develop and maintain Tekton CI/CD pipelines for analytics code, data pipelines, and infrastructure changes.
4. Implement event-driven processing patterns using Cloud Functions triggered by bucket events, Pub/Sub, or HTTP.
5. Engineer Cloud Storage bucket strategies, including naming, IAM, lifecycle policies, retention, and cost-aware tiering.
6. Apply strong data governance practices: data quality checks, metadata standards, lineage, and documentation.
7. Support model and pipeline validation for data integrity, reliability, and production readiness.
8. Build dashboards and reports that highlight trends, anomalies, and operational insights for engineering teams.
9. Collaborate with calibration, controls, test, IT, and cybersecurity stakeholders to define and deliver requirements.
10. Contribute to DevSecOps practices by integrating security scanning, secret management, and release controls into CI/CD.
11. Monitor production services and pipelines using Cloud Logging, Monitoring, and alerting, and support incident response.
12. Ensure compliance with enterprise data security, cybersecurity, and operational standards.

1. Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field.

Experience

1. 1-5+ years of relevant experience in data engineering, analytics engineering, data analysis, or data science.
2. Hands-on delivery experience with data processing, analysis, and visualization.
3. Experience with cloud-based analytics or data platforms; GCP experience strongly preferred.
4. Automotive and powertrain data experience is a strong advantage.

Core Data and Analytics

1. Python proficiency for data transformation, automation, and analytics workflows.
2. Strong SQL skills for extraction, transformation, and analytical querying.
3. Solid understanding of ETL/ELT, data modeling, and data warehousing concepts.
4. Experience with BI tools such as Tableau, Power BI, or Qlik.
5. Ability to analyze large and complex datasets and produce actionable engineering insights.
6. Version control proficiency with Git and collaborative branching/review workflows.

GCP Technologies (High Emphasis)

1. Cloud Storage: bucket architecture, IAM controls, lifecycle rules, retention policies, event notifications, and storage classes.
2. Cloud Functions: event-driven development, deployment, observability, retry handling, and idempotent processing.
3. Tekton: pipeline authoring, reusable tasks, triggers, artifact traceability, automated tests, approvals, and promotion gates.
4. BigQuery and Pub/Sub: ingestion, transformation, and event-stream integration in analytics workflows.
5. Identity and Access Management (IAM): least-privilege role design, service account scoping, and access governance.
6. Security controls: Secret Manager, Cloud KMS, audit logging, and secure configuration baselines.
7. Monitoring and reliability: Cloud Logging, Cloud Monitoring, Error Reporting, and alert-driven operations.
8. Infrastructure as Code: Terraform or equivalent for repeatable GCP provisioning.

DevOps and Cybersecurity

1. CI/CD implementation for analytics and data products across dev, test, and prod environments.
2. DevSecOps integration including dependency scanning, secret scanning, and policy checks in pipelines.
3. Release management with rollback strategy, quality gates, and deployment governance.
4. Working knowledge of cloud threat detection and security posture management principles.

Collaboration Skills (Soft Skills)

1. Strong written and verbal communication; clear technical documentation and stakeholder reporting.
2. Structured problem solving with accountability and attention to root-cause analysis.
3. High learning agility and adaptability in fast-evolving cloud and analytics environments.
4. Effective cross-functional collaboration in global teams.
5. Detail orientation and ownership mindset for data quality, reliability, and security.