Senior Platform Engineer managing AWS, Kubernetes, Terraform, and CI/CD infrastructure at Ledgebrook. Building secure self - service platforms that improve software delivery reliability.
About the role
- Data platforms engineer modernizing General Motors’ Oracle workloads through cloud migration. Building secure, governed cloud data platforms, pipelines, infrastructure automation, and operational practices.
Responsibilities
- Assess existing Oracle-based data architecture, workloads, dependencies, interfaces, data flows, and operational processes for migration planning and execution
- Design and implement scalable batch and near-real-time data pipelines for ingestion, transformation, validation, reconciliation, and publishing
- Develop migration patterns for data, schemas, ETL/ELT workloads, stored procedures, interfaces, and downstream consumers
- Build and maintain cloud data-platform capabilities across Azure, Google Cloud Platform, or multi-cloud environments
- Use Databricks capabilities including Workflows, Unity Catalog, Delta Lake, MLflow, and Asset Bundles where appropriate
- Develop reusable Terraform infrastructure as code for cloud, networking, data-platform, and Databricks resources
- Design and support CI/CD workflows and automate deployment and environment promotion across development, test, staging, and production
- Implement data security, identity and access management, encryption, secrets management, key rotation, and least-privilege controls
- Establish data governance practices including cataloging, lineage, classification, retention, auditability, and responsible data use
- Design and maintain monitoring, logging, alerting, data-quality checks, and operational dashboards
- Improve reliability, scalability, performance, and cost efficiency through automation and continuous optimization
- Lead or support incident response, service recovery, root cause analysis, and post-incident reviews
- Define and support SLAs, SLOs, freshness objectives, recovery objectives, and error-budget-aware practices
- Develop automated unit, integration, data-quality, regression, and end-to-end tests
- Collaborate with architects, application teams, analytics and AI practitioners, security partners, product owners, and global technology teams
- Participate in two-week sprints, backlog refinement, estimation, delivery planning, demos, and continuous improvement
- Create and maintain architecture diagrams, data-flow documentation, API and interface documentation, runbooks, onboarding guides, README files, and production-readiness materials
Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Engineering, or a related discipline, or equivalent practical experience
- At least 3 years of professional experience in data engineering, cloud platform engineering, DevOps, software engineering, or a related field
- Demonstrated experience delivering production data solutions
- Hands-on experience designing and supporting data pipelines and ETL/ELT workloads, preferably involving Oracle or another enterprise relational database platform
- Strong Python experience for automation, developer tooling, data engineering, and PySpark-based processing
- Experience with SQL, relational data modeling, schema design, query optimization, and data reconciliation
- Experience with at least one major cloud platform, preferably Azure or Google Cloud Platform
- Experience with infrastructure as code using Terraform, including reusable modules, remote state, and environment-specific configuration
- Experience with Git, branching strategies, pull requests, code reviews, and automated CI/CD practices
- Experience with a cloud data platform or lakehouse technology such as Databricks, Delta Lake, BigQuery, Synapse, or an equivalent platform
- Understanding of cloud networking, identity and access management, encryption, secrets management, and secure service-to-service integration
- Experience implementing monitoring, logging, alerting, operational dashboards, and data-quality controls
- Strong troubleshooting, analytical, communication, and cross-functional collaboration skills
- Preferred: experience migrating Oracle databases, ETL jobs, stored procedures, or data warehouses to cloud services
- Preferred: hands-on experience with Databricks Workflows, Unity Catalog, Delta Lake, MLflow, and Asset Bundles
- Preferred: Azure services including ADLS Gen2, Key Vault, Entra ID, AKS, Azure networking, and Azure DevOps or GitHub Actions
- Preferred: Google Cloud services including Cloud Storage, BigQuery, Dataproc, GKE, Pub/Sub, Secret Manager, Cloud IAM, and VPC networking
- Preferred: streaming and event-driven data processing using Apache Kafka, Apache Pulsar, Pub/Sub, or equivalent technology
- Preferred: Kubernetes, Azure Container Apps, GKE, or other containerized runtime environments
- Preferred: Datadog or another observability platform
- Preferred: MLflow, model or feature pipelines, experiment tracking, and production monitoring for AI/ML workloads
- Preferred: HashiCorp Vault or comparable enterprise secrets-management tools
- Preferred: experience in regulated, security-sensitive, or enterprise-scale environments
- Preferred: production readiness reviews, incident management, postmortems, and service reliability practices
- Must not require GM immigration-related sponsorship now or in the future
Benefits
- Hybrid work arrangement
- Opportunities to work on cloud data platforms, analytics, artificial intelligence, and enterprise modernization
- Inclusive workplace fostering belonging and professional development
- Role-related assessment and/or pre-employment screening information provided
- Accommodation support for applicants with disabilities
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