As a Data Architect, youll sit at the intersection of data engineering, ML systems, and platform architecture. Youll own the patterns, guardrails, and data platform capabilities that let teams ship AI-native, low-latency experiences at scale-safely, reliably, and cost-effectively.
What Youll Actually Do:
Design AI-native data systems: LLM/RAG pipelines, embeddings & vector search, and real-time inference- production-grade and observable.
Evolve the data platform: Batch + streaming + lakehouse; CDC, orchestration, lineage/quality, and clear data contracts for ML readiness.
Set org standards: Contract-first APIs & event schemas, ADRs, SLOs (latency/MTTR/cost); lead design reviews and architecture spikes.
Modernize pragmatically: Guide adoption of Databricks, Kafka, Airflow, Kubernetes, Terraform, and modern observability- fit to purpose.
Lead by influence: Mentor Tech Leads, partner with Product/ML/Platform, and turn goals into resilient, measurable systems.
Requirements:
5+ years as a Software/Data/Solution Architect in AI-intensive or data-heavy environments; ~10+ years engineering overall.
Distributed systems depth: microservices, event-driven design, backpressure/idempotency, retries/DLQs; contract-first APIs.
Data platform expertise: streaming + batch + lakehouse, CDC, orchestration, governance/lineage, schema evolution.
AI systems fluency: LLMs, embeddings, vector stores, RAG; real-time production inference.
Hands-on: Python or TypeScript/Scala; Databricks, Airflow, Kafka, Kubernetes, Terraform; Prometheus/Grafana/Coralogix.
Cloud-first (AWS preferred), security-by-design, crisp writing and collaboration.
5+ years as a Software/Data/Solution Architect in AI-intensive or data-heavy environments; ~10+ years engineering overall.
Distributed systems depth: microservices, event-driven design, backpressure/idempotency, retries/DLQs; contract-first APIs.
Data platform expertise: streaming + batch + lakehouse, CDC, orchestration, governance/lineage, schema evolution.
AI systems fluency: LLMs, embeddings, vector stores, RAG; real-time production inference.
Hands-on: Python or TypeScript/Scala; Databricks, Airflow, Kafka, Kubernetes, Terraform; Prometheus/Grafana/Coralogix.
Cloud-first (AWS preferred), security-by-design, crisp writing and collaboration.
This position is open to all candidates.
