Data Architect

Here at Lusha, we power sales with data. Over 1.5M users and teams at Google, Spotify, and Elastic use our platform to find verified contacts, spot real-time buying signals, and automate workflows with 200M+ records.

As a Data Architect, you’ll sit at the intersection of data engineering, ML systems, and platform architecture. You’ll 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 You’ll 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.
Bonus: Serving/fine-tuning LLMs, MLOps/AIOps, OSS contributions, public talks/blog posts.

Why Lusha:

AI is the product: Your architecture directly shapes core user experiences at a meaningful scale.
Impact without red tape: Own decisions, move fast, see results.
Culture of excellence: Design-first, measurement-driven, privacy-minded, and highly collaborative.
Modern stack, real autonomy: Build with the right tools—not buzzwords.
Growth & visibility: Lead company-wide standards, mentor future leaders, and raise the bar across ML/Data/Platform.