Engineer Jobs

About The Position

Requirements

• Bachelor's degree in a related field or equivalent practical experience; the posting indicates 5–7+ years.
• Professional experience building and operating production data platforms, data infrastructure, or backend systems.
• Strong programming skills in at least one systems or data language such as Python, Go, or Rust, with a record of shipping maintainable, tested code.
• Experience building reusable connectors, ingestion frameworks, or self-service source-onboarding capabilities for direct use by non-platform users.
• Production experience with graph databases, graph processing frameworks, or graph query languages such as Spanner Graph, Neo4j, TigerGraph, GQL, Cypher, Gremlin, or SPARQL.
• Experience with relational and columnar systems, document or key-value stores, search indexes, time-series platforms, or vector retrieval systems; examples include BigQuery, Spanner, AlloyDB, Cloud SQL, Bigtable, Firestore, Milvus, and Vertex AI Vector Search.
• Knowledge of indexing approaches such as adjacency, B-tree, LSM-tree, inverted, bitmap, and approximate nearest-neighbor indexes, and experience using profiling and benchmarking to achieve measurable performance improvements.
• Experience with metadata catalogs, schema registries, lineage, data discovery, or federated data-product platforms.
• Familiarity with cloud infrastructure, Kubernetes, observability platforms, and infrastructure-as-code; hands-on Google Cloud Platform experience, including GKE, IAM, Cloud Monitoring and Logging, and Terraform, is expected.
• Experience in automotive, manufacturing, engineering, supply chain, or another complex, data-intensive environment.
• Practical experience applying AI coding assistants and agentic tools to specific engineering workflow tasks, with the ability to verify their output.

Nice To Haves

• Master's degree in Computer Science, Data Engineering, or a related field.
• Experience with streaming and change-data-capture pipelines such as Pub/Sub, Dataflow, Datastream, Kafka, or Flink for near-real-time ingestion.
• Experience with industrial data sources and protocols such as OPC UA, MQTT, historians, or MES and PLM systems.
• Experience building retrieval infrastructure for AI/ML workloads on Vertex AI, including embedding pipelines, vector search, hybrid search, or retrieval-augmented generation.
• Experience applying large language models to data engineering tasks such as schema inference, entity resolution, data mapping, or automated data quality checks.
• Experience shipping AI-powered developer or data tooling to end users, with human review, sandboxing, and guardrails for safe deployment of generated code.
• Experience with agent frameworks and tool-use protocols such as Model Context Protocol, or exposing data platforms as tools for AI agents.
• Experience designing evaluation frameworks for LLM-based systems, including retrieval quality metrics and regression testing.
• Experience with query engines and open table formats such as BigQuery, BigLake, Dataproc and Spark, Trino, or Apache Iceberg.
• Experience designing data products or APIs consumed by multiple teams across a large enterprise.
• Strong written and verbal communication skills, including the ability to explain architectural trade-offs to non-technical stakeholders.

Responsibilities

• Design reusable connectors, ingestion frameworks, and self-service onboarding tools so sources can be registered, profiled, and queried through configuration rather than bespoke code.
• Design graph and relational schemas linking parts, plants, processes, suppliers, equipment, and quality events to support cross-domain queries.
• Select and implement indexing strategies and query execution paths that meet latency and throughput targets as data volumes grow.
• Profile, benchmark, and tune performance across source connection, ingestion, indexing, discovery, and retrieval.
• Integrate metadata catalogs, schema registries, and lineage tracking so connected sources are findable, documented, and traceable.
• Embed data quality checks, access control, governance policies, and lineage capture into ingestion and retrieval.
• Define service-level objectives for retrieval latency, freshness, and onboarding time, and build observability using Cloud Monitoring and Cloud Logging.
• Deploy on Google Kubernetes Engine and Cloud Run using Terraform, support the production platform, and resolve root causes.
• Design capabilities that can be scaled globally from an initial plant deployment without re-engineering.
• Use AI coding assistants and large language models for appropriate repetitive engineering tasks, with validation and engineer review.
• Build self-service, AI-driven connector creation experiences, APIs, and agent workflows with governed deployment guardrails.
• Build AI retrieval capabilities, including Vertex AI embedding pipelines, hybrid search across Vertex AI Vector Search and BigQuery, and graph-aware retrieval.
• Create evaluation harnesses and benchmarks for AI-assisted onboarding and retrieval quality, measuring precision, recall, and latency against real workloads.
• Set technical direction for connectivity and retrieval, author design documents, lead code and design reviews, and uphold performance and reliability standards.
• Partner with data producers, analytics teams, and AI/ML teams to understand access patterns and translate them into platform priorities.

Benefits

• Immediate medical, dental, vision, and prescription drug coverage.
• Flexible family care days, paid parental leave, new parent ramp-up programs, and subsidized backup childcare.
• Family-building benefits, including adoption and surrogacy expense reimbursement and fertility treatments.
• Vehicle discount program for employees and family members, and management leases.
• Tuition assistance.
• Employee resource groups.
• Paid time off for individual and team community service.
• Paid holidays, including the week between Christmas and New Year's Day.
• Paid time off and the option to purchase additional vacation time.