layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Senior Data Engineer at General Motors | LAYIQ

Design and develop production-grade batch and real-time data pipelines for connected-vehicle telemetry, trip and session data, diagnostic signals, and vehicle-health indicators.
Build streaming applications that ingest, enrich, validate, deduplicate, curate, and publish event-driven data for downstream services, notifications, reporting, and analytics.
Develop reliable data products using Apache Flink, Apache Spark Structured Streaming, Java, Python, and SQL.
Work with Azure services including Azure Kubernetes Service, Event Hubs, Azure Data Explorer, Azure Key Vault, Azure Databricks, Azure Monitor, and Application Insights.
Design and maintain data contracts, schemas, APIs, and event models using GraphQL, REST, gRPC, JSON, and cloud-event patterns.
Apply artificial intelligence and machine learning to data engineering problems such as anomaly detection, data-quality triage, predictive health signals, intelligent operations, and engineering productivity.
Build or integrate generative artificial intelligence capabilities, including large language model applications, embeddings, vector search, retrieval-augmented generation, agentic workflows, prompt engineering, evaluation, and safety guardrails.
Create automated tests, performance benchmarks, integration tests, and validation checks for high-volume data and event-driven systems.
Establish observability with OpenTelemetry, Datadog, Grafana, Prometheus, dashboards, monitors, service-level objectives, and actionable alerts.
Secure data in transit and at rest and apply privacy, consent, retention, lineage, access-control, and regional compliance requirements to vehicle and location data.
Automate infrastructure and delivery using Kubernetes, Helm, Argo CD, Terraform, continuous integration, continuous delivery, and infrastructure-as-code practices.
Participate in architecture reviews, code reviews, incident response, root-cause analysis, operational readiness, and on-call support as needed.
Mentor engineers, raise technical standards, document design decisions, and contribute to a culture of quality, ownership, and continuous improvement.
Your Skills & Abilities (Required Qualifications)
Bachelor’s degree in computer science, computer engineering, data engineering, information systems, or a related technical field, or equivalent experience.
5+ years of professional experience in data engineering, software engineering, distributed systems, or a related field.
Strong hands-on experience with Java or Python, SQL, object-oriented design, data structures, algorithms, and automated testing.
Experience designing and operating production data pipelines using Apache Flink, Apache Spark, Kafka, Azure Event Hubs, or comparable streaming technologies.
Experience with cloud-native development on Microsoft Azure and containerized workloads running on Kubernetes.
Experience with Databricks, Delta Lake, distributed data processing, data modeling, and performance optimization.
Experience designing APIs and event-driven systems using GraphQL, REST, gRPC, asynchronous HTTP clients, or equivalent technologies.
Experience with schema evolution, data contracts, data-quality validation, lineage, observability, and privacy-aware data handling.
Demonstrated experience applying machine learning, artificial intelligence, or generative artificial intelligence in a production engineering, analytics, or data-product environment.
Working knowledge of large language models, embeddings, vector databases or vector search, retrieval-augmented generation, prompt design, model evaluation, and responsible artificial intelligence practices.
Ability to troubleshoot complex distributed systems and communicate technical decisions clearly to both technical and nontechnical audiences.
Ability to work effectively in a collaborative, agile, cross-functional environment.

Senior Data Engineer

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