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À propos
Own and design the backend architecture of a scalable AI-driven data analytics SaaS (Ingestion → Processing → Query → AI Insights).
Responsibilities
Design and lead the Data & Compute Layer using DuckDB, Pandas/Polars, and Spark
Architect scalable data ingestion and processing pipelines
Build high-performance query execution systems (in-memory + distributed)
Design robust job/worker systems (queues, retries, idempotency, backpressure)
Ensure performance at scale (large datasets, high concurrency, p95/p99 targets)
Implement multi-tenant isolation and resource management
Guarantee production readiness (observability, security, cost efficiency)
Requirements (Google-Level)
10+ years experience in distributed backend/data systems
Deep expertise in:
DuckDB (embedded analytics, columnar engines)
Pandas or Polars (data transformation layer)
Apache Spark (distributed computation)
Strong knowledge of PostgreSQL, Kafka/SQS, Redis, Kubernetes
Proven ownership of high-scale SaaS architectures
Thinks in failure modes, data integrity, scalability, and system trade-offs
Profile: Staff/Principal-level engineer capable of designing modern data backends at Google/Snowflake-grade scale and reliability.
Contract duration of 1 to 3 months. with 40 hours per week.
Mandatory skills: Big Data, backend, Azure DevOps, Python
Compétences linguistiques
- English
Avis aux utilisateurs
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