לוח משרות דרושים הייטק דאטה בבנימינה גבעת עדה

Required Data Infrastructure Engineer
What Youll Do:
Design, implement, and enhance robust and scalable infrastructure that enables efficient deployment, monitoring, and management of machine learning models in production. In this role, you will bridge the gap between research and production environments, streamline data and feature pipelines, optimize model serving, and ensure governance and reproducibility across our ML lifecycle.
Responsibilities:
Decouple data prep from model training to accelerate experimentation and deployment
Build efficient data workflows with versioning, lineage, and optimized resource use (e.g., Snowflake, Dask, Airflow)
Develop reproducible training pipelines with MLflow, supporting GPU and distributed training
Automate and standardize model deployment with pre-deployment testing (E2E, dark mode)
Maintain a model repository with traceability, governance, and consistent metadata
Monitor model performance, detect drift, and trigger alerts across the ML lifecycle
Enable model comparison with A/B testing and continuous validation
Support infrastructure for deploying LLMs, embeddings, and advanced ML use cases
Manage a unified feature store with history, drift detection, and centralized feature/label tracking
Establish a single source of truth for features across research and production across research and production.

Required Data Engineer
As part of our Data Engineering team, you will not only build scalable data platforms but also directly enable portfolio growth by supporting new funding capabilities, loan sales and securitization, and improving cost efficiency through automated and trusted data flows that evolve our accounting processes.
Responsibilities:
Design and build data solutions that support our core business goals, from enabling capital market transactions (loan sales and securitization) to providing reliable insights for reducing the cost of capital.
Develop advanced data pipelines and analytics to support finance, accounting, and product growth initiatives.
Create ELT processes and SQL queries to bring data to the data warehouse and other data sources.
Develop data-driven finance products that accelerate funding capabilities and automate accounting reconciliations.
Own and evolve data lake pipelines, maintenance, schema management, and improvements.
Create new features from scratch, enhance existing features, and optimize existing functionality.
Collaborate with stakeholders across Finance, Product, Backend Engineering, and Data Science to align technical work with business outcomes.
Implement new tools and modern development approaches that improve both scalability and business agility.
Ensure adherence to coding best practices and development of reusable code.
Constantly monitor the data platform and make recommendations to enhance architecture, performance, and cost efficiency.