Senior Staff Engineer-Data and AI Platform

Responsibilities

• Architect and lead the end-to-end migration of enterprise data workloads and storage from AWS and Databricks to a centralized, modernized GCP ecosystem.
• Establish dual-run strategies, data validation frameworks, and zero-downtime cutover methodologies to ensure business continuity.
• Design multi-tenant, secure, and cost-optimized cloud architectures leveraging BigQuery, Dataflow, Dataproc, and Cloud Storage.
• Design, build, and evangelize reusable, metadata-driven ingestion, transformation, and serving frameworks.
• Standardize pipeline development with built-in data quality, lineage, automated schema evolution, and enterprise RBAC/governance.
• Create high-performance batch and real-time streaming architectures supporting mission-critical analytics and operational workloads.
• Build self-service SDKs, templates, and CLI tools that abstract cloud infrastructure complexities for platform consumers (DEs, BIs, DSs).
• Drive CI/CD automation and Infrastructure as Code (IaC) best practices to accelerate time-to-delivery for data and machine learning products.
• Enhance observability, monitoring, alerting, and cost-governance tooling across all data pipelines and AI workloads.
• Architect and scale enterprise AI and MLOps platforms spanning traditional ML, Deep Learning, and Generative AI (LLMs).
• Implement robust model lifecycle infrastructure including feature stores, model registries, automated CI/CD for ML pipelines, and real-time/batch inference engines.
• Standardize LLMOps foundations, including vector database integration, retrieval-augmented generation (RAG) frameworks, model evaluation pipelines, and fine-tuning infrastructure.
• Set technical direction, architectural standards, and engineering guardrails across data and AI engineering teams.
• Mentor senior and staff engineers, fostering technical excellence, innovation, and cross-functional collaboration.
• Partner with Product, Security, Compliance, and Business Leadership to align platform roadmap priorities with core business outcomes.

Requirements

• 10+ years of software, data, or platform engineering experience, including 3+ years at the Staff or Senior Staff level.
• Proven success leading large-scale cloud and enterprise data platform migrations.
• Deep expertise in GCP, including BigQuery, Dataflow, Dataproc, Airflow/Composer, Pub/Sub, and GCS.
• Strong experience with Terraform, Kubernetes, and cloud-native infrastructure.
• Hands-on experience building production AI/ML and MLOps platforms using Vertex AI, Kubeflow, MLflow, Feature Stores, and related technologies.
• Knowledge of GenAI, LLMOps, vector databases, RAG, model monitoring, and AI governance.
• Expert proficiency in Python, SQL, Spark, Trino/Presto, and dbt.
• Strong background in distributed systems, data architecture, data modeling, and platform engineering.
• Experience implementing enterprise security, governance, RBAC/ABAC, lineage, auditing, and compliance frameworks.
• Exceptional communication, technical leadership, and stakeholder management skills.
• Computer Science degree or comparable formal training, certification, or work experience.

Preferred Qualifications

• Experience with AWS, Databricks, Delta Lake, and Apache Iceberg.
• Experience delivering enterprise-scale cloud modernization and platform transformation initiatives.
• Hands-on experience with production Generative AI, LLM, RAG, and AI Agent solutions.
• Background in retail, supply chain, e-commerce, or other large-scale digital organizations.
• Experience building self-service data and AI platforms and mentoring senior engineering talent.