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.
