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About the role
10+ years of experience in software development and data engineering, with at least 5 years in a senior technical leadership role, preferably as a Principal Developer or Data Architect.
Demonstrated experience architecting and enabling data for AI workflows in production, such as embedding pipelines, vector-based retrieval, RAG data workflows, and real-time/event-driven data flows that support agentic systems and AI integrations.
Experience with AI platform integration and orchestration patterns, including agent workflow orchestration and LLM/agent observability and evaluation, partnering closely with data science and engineering teams to operationalize AI-Ready datasets and pipelines. Familiarity with LangGraph, Langfuse, MCP, AWS Bedrock, AWS AgentCore, and LaunchDarkly is preferred.
Experience enabling secure interoperability patterns with customer AI systems, including governed data access, tenant-aware controls, and safe integration patterns for customer-managed AI workflows.
Experience defining AI and data governance and platform adoption standards in large organizations, including controls for privacy, access, auditability, safe reuse, and operational guardrails for AI-Ready datasets and data products.
Experience designing modern data platforms on cloud-native infrastructure (AWS preferred), including lakehouse patterns, medallion architecture, ETL/ELT pipelines, distributed processing with Spark, Trino, and MapReduce, analytics and AI-Ready data at scale, with strong operational practices.
Hands-on experience with core data technologies and integration patterns: MongoDB, Amazon DocumentDB, MS SQL Server, DynamoDB, AWS ElastiCache for Redis or Valkey; event streaming and queueing using SNS/SQS. Postgres, pgvector, and Kafka or Pub/Sub are an asset.
Hands-on experience with AWS data platform services: S3, S3 Express, Athena, Glue Catalog, Lake Formation, OpenSearch Serverless, S3 Vector Storage, Iceberg, Lambda, Step Functions, EKS, ETL on EMR, and EMR Serverless.
Proven ability to architect and deliver scalable, reliable data systems and product data architectures, guiding teams in data models, storage and integration architectures, data contracts, data domain taxonomy, schema and event versioning, and resolving performance and scale bottlenecks.
Proficiency in data movement and performance architecture: Experience designing replication, event sourcing, and CDC/change tracking strategies, safe historical reprocessing patterns, and performance optimization through query analysis, indexing, and partitioning.
Strong technical leadership: Experience mentoring teams, setting engineering and architecture standards, and influencing technical direction across multiple teams.
Experience working with DevOps teams, CI/CD pipelines, infrastructure-as-code, and operational tooling to deliver scalable, resilient data platforms and pipelines.
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