Big Data & Data Infrastructure Engineer (AI)

Location: Tel Aviv, Israel

Company

hirify.global builds enterprise data platform infrastructure for capturing, cataloging, refining, enriching, protecting, and analyzing massive datasets for AI training and inference across data centers, edge, and cloud environments.

What you will do

• Build distributed batch and streaming data pipelines using Kafka, Spark, Python, Trino, Airflow, and S3-compatible data lakes.
• Design, deploy, troubleshoot, and optimize hybrid cloud and on-premises environments with Terraform, Docker, Kubernetes, and CI/CD automation.
• Implement event-driven and serverless workflows while managing latency, throughput, and fault-tolerance trade-offs.
• Own the data platform lifecycle from ingestion and transformation through S3-based storage and Trino/Spark compute layers.
• Benchmark and tune S3, NFS, and SMB storage backends and analytics engines using production datasets.
• Create architecture documentation, technical guides, demo pipelines, observability, validation, and governance capabilities while collaborating with engineering, product, R&D, and customers.

Requirements

• 2–4 years of experience in software, solutions, or infrastructure engineering, including work with large-scale data pipelines, storage, or database solutions.
• Proficiency in Trino and Spark for structured streaming and batch workloads, plus solid knowledge of Apache Kafka.
• Strong Python development skills; experience with Bash and scripting tools is beneficial.
• Understanding of SQL, NoSQL, HDFS, distributed systems, stream processing, and event-driven architecture.
• Experience with Docker, Kubernetes, Terraform, benchmarking, and performance profiling for storage systems, databases, and analytics engines.
• Strong communication skills for explaining technical decisions, guiding customer conversations, and collaborating across engineering and product teams.

Culture & Benefits

• Work on enterprise infrastructure for the AI era and large-scale analytics workloads.
• Collaborate with engineering, product, R&D, operations, business development, and customer-facing teams.
• Contribute to performance-focused systems spanning interactive, streaming, and ML-ready analytics.