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במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
28,000-40,000 ₪
we are looking for a Staff Engineer (backend oriented - python).
As a Staff Engineer, you will own and expand a critical backend data processing platform that sits between data ingestion and the customer-facing application. This platform powers features such as data enrichment, grouping/similarity, and internal analytics. You will lead Python services that transform raw financial data into product-ready signals used by customers every day.
Platform Evolution: Increase enrichment accuracy and coverage by evolving data processing logic and adding new capabilities.
Performance Engineering: Reduce processing latency and improve throughput by designing efficient message-driven pipelines.
API & Schema Design: Maintain clear, stable service interfaces (HTTP/gRPC) and schemas with strong backward compatibility.
Data Modeling & Querying: Model data and write efficient Postgres queries; strategically use vectors/embeddings where they add value.
Tooling: Build internal analytics/debugging tools to accelerate triage and insight for engineering and operations teams.
Quality & Standards: Raise the quality bar through component/integration/snapshot tests, thorough code reviews, documentation, and release hygiene.
Observability: Instrument services fully by shipping meaningful metrics and logs, and using dashboards to troubleshoot and proactively improve reliability
As a Staff Engineer, you will own and expand a critical backend data processing platform that sits between data ingestion and the customer-facing application. This platform powers features such as data enrichment, grouping/similarity, and internal analytics. You will lead Python services that transform raw financial data into product-ready signals used by customers every day.
Platform Evolution: Increase enrichment accuracy and coverage by evolving data processing logic and adding new capabilities.
Performance Engineering: Reduce processing latency and improve throughput by designing efficient message-driven pipelines.
API & Schema Design: Maintain clear, stable service interfaces (HTTP/gRPC) and schemas with strong backward compatibility.
Data Modeling & Querying: Model data and write efficient Postgres queries; strategically use vectors/embeddings where they add value.
Tooling: Build internal analytics/debugging tools to accelerate triage and insight for engineering and operations teams.
Quality & Standards: Raise the quality bar through component/integration/snapshot tests, thorough code reviews, documentation, and release hygiene.
Observability: Instrument services fully by shipping meaningful metrics and logs, and using dashboards to troubleshoot and proactively improve reliability
Requirements:
Core Backend Expertise: Strong Python 5+ backend experience, including building production services with a focus on clean code and maintainability.
Asynchronous Systems: Experience with message-driven systems (e.g., AWS SQS or similar) and background workers.
Database Proficiency: Solid SQL and Postgres fundamentals; comfort with schema evolution and performance basics.
API & Protocols: Service API fundamentals: HTTP and gRPC; awareness of protobuf/versioning and backward compatibility practices.
DevOps Comfort: Familiarity with CI/CD as a user (e.g., GitHub Actions) and comfortable with branch-based test environments.
Operational Mindset: Strong observability mindset: the ability to add metrics, read logs/dashboards, and debug issues systematically.
Testing Discipline: Confident with testing (pytest, component/integration tests, snapshot testing) and confident in refactoring existing code.
Ownership: Demonstrated end-to-end responsibility for a platform area and effective collaboration with adjacent teams (Application, Data Science).
Nice-to-haves:
Applied Data/ML Engineering: Practical experience with pandas, scikit-learn, onnxruntime, numba, and production inference experience.
Vector/Similarity: Practical work with embeddings/similarity (e.g., pgvector) for grouping and search functionalities.
Internal Tools: Experience building internal analytics tools (e.g., Streamlit) for triage and insight.
Advanced gRPC: Deeper gRPC/protobuf practices, including schema evolution strategies and performance tuning.
Security in Data: Experience with security/privacy in data-heavy domains (PII handling, secrets hygiene, auditing best practices).
Domain Knowledge: Experience integrating with ERP systems or financial data sources.
Core Backend Expertise: Strong Python 5+ backend experience, including building production services with a focus on clean code and maintainability.
Asynchronous Systems: Experience with message-driven systems (e.g., AWS SQS or similar) and background workers.
Database Proficiency: Solid SQL and Postgres fundamentals; comfort with schema evolution and performance basics.
API & Protocols: Service API fundamentals: HTTP and gRPC; awareness of protobuf/versioning and backward compatibility practices.
DevOps Comfort: Familiarity with CI/CD as a user (e.g., GitHub Actions) and comfortable with branch-based test environments.
Operational Mindset: Strong observability mindset: the ability to add metrics, read logs/dashboards, and debug issues systematically.
Testing Discipline: Confident with testing (pytest, component/integration tests, snapshot testing) and confident in refactoring existing code.
Ownership: Demonstrated end-to-end responsibility for a platform area and effective collaboration with adjacent teams (Application, Data Science).
Nice-to-haves:
Applied Data/ML Engineering: Practical experience with pandas, scikit-learn, onnxruntime, numba, and production inference experience.
Vector/Similarity: Practical work with embeddings/similarity (e.g., pgvector) for grouping and search functionalities.
Internal Tools: Experience building internal analytics tools (e.g., Streamlit) for triage and insight.
Advanced gRPC: Deeper gRPC/protobuf practices, including schema evolution strategies and performance tuning.
Security in Data: Experience with security/privacy in data-heavy domains (PII handling, secrets hygiene, auditing best practices).
Domain Knowledge: Experience integrating with ERP systems or financial data sources.
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
