Data Engineer

Company Description

WideOps is a leading provider of cloud infrastructure management and monitoring solutions. We empower organizations to efficiently manage their cloud resources, identify issues, and gain insights. Our service enables businesses to optimize their cloud usage, improve security, and achieve greater operational efficiency.

Role Description

This is a full-time, on-site role for a Data Engineer at WideOps Ltd., located in Haifa. The Data Engineer will design, build, and maintain scalable data pipelines, implement robust Extract, Transform, Load (ETL) processes, and develop efficient data models to support business intelligence and data analytics needs. The role also involves building and improving data warehousing solutions and collaborating with cross-functional teams to ensure data integrity and optimize workflows.

Qualifications

Core Data Engineering

• 3+ years of experience as a Data Engineer
• Strong hands-on experience with Google Cloud Platform
• Advanced SQL (BigQuery performance, partitioning, clustering)
• Strong Python (data pipelines, APIs, batch & streaming jobs)
• Experience building batch and streaming pipelines
• BigQuery
• Dataflow / Apache Beam
• Pub/Sub
• Cloud Storage
• Data modeling for analytics and ML use cases
• Production troubleshooting, optimization, and cost awareness

ML & GenAI

• Experience supporting ML pipelines (training, inference, feature prep)
• Hands-on with at least one of:
• Vertex AI
• Feature stores
• Model inference pipelines

• Experience working with LLM-based systems, including:
• Embeddings generation
• Vector databases / similarity search
• RAG-style pipelines
• Ability to prepare, version, and serve data for GenAI / ML workloads

Platform & Engineering

• Experience with Airflow / Cloud Composer or equivalent orchestration
• CI/CD pipelines for data & ML workloads
• Infrastructure-as-code familiarity (Terraform preferred)
• Understanding of IAM, data security, and access controls on GCP

Nice to Have

• Experience with FinOps / cost optimization
• Experience with real-time ML or low-latency inference
• Experience with monitoring & observability for data/ML pipelines
• Google Cloud certifications (Data Engineer / Architect)