Developer

About the Company Alvar Financial Services is a boutique firm offering a variety of investment and trading services through its business lines - Equity Markets for professional and institutional clients, Research Services and Electronic Global Retail Platform for private clients. Our small community and flat structure empowers our people to grow and create unconstrained ideas within their field of expertise, driving innovation and mastery in their work. We all work together as one to achieve our vision of being a trusted partner to our clients, providing unrivalled insights and a collaborative high touch service. About the Role We are looking for an experienced, highly motivated Python developer with expertise in building robust data pipelines, creating and manipulating DataFrames, and turning defined specifications into reliable, production grade scripts. The role centres on developing and orchestrating the data infrastructure behind a systematic research platform: document analysis engines that extract structured data from financial filings and disclosures, integrated with market and transaction datasets into a point in time research database. This is a genuine opportunity to extend into quantitative research and to help build the foundation for full stack, next gen SaaS service offerings. Responsibilities: Bulletproof script development: build reliable, production grade scripts from defined specifications.Data orchestration and integration: develop and own the orchestration scripts that integrate multiple data feeds (document analysis output, transaction data, market data) into the existing point in time database on a reliable schedule, continuously improving the dataset's quality and coverage.Document analysis engines: develop and improve engines that turn financial filings and disclosures into structured, queryable data, including LLM assisted extraction where appropriate.Testing to production migration: own the path from development to production, testing rigorously in a staging environment before migrating scripts to the production server with proper versioning, rollback capability and zero disruption to live processes.Data hygiene, processing and transformation: apply disciplined data hygiene across sources, including deduplication, entity and identifier mapping, and handling of missing or malformed values, with close attention to point in time integrity (as of date versioning, no look ahead bias, clean handling of revisions).Automation and optimisation: automate data workflows and build in data quality checks so parsing failures, gaps or stale data are caught before they reach research.Documentation and code quality: write well documented, maintainable code with unit tests covering data processing and integration scripts. Clear, thorough documentation of what's built, how it works and how to run it is a core part of the role, so work can be understood, maintained and handed over by others.Foundation development: contribute to backend API development, user authentication and database integration, laying groundwork for a scalable SaaS platform.Any other ad hoc duties that may be required. Technical Requirements: Proven experience with Python, especially Pandas and NumPyMastery of SQL and PostgreSQL: schema design, complex queries, indexing and performance optimisation, beyond basic queryingExperience with pipeline orchestration and schedulingProven ability to deliver reliable, tested scripts against a written specification, and to migrate them cleanly from a test environment to a production serverStrong data hygiene, processing and cleaning skills, including experience with messy real world data (documents, filings, PDFs, HTML, third party APIs)A habit of documenting your own work thoroughly, so others can understand, maintain and build on itLLM API experience for structured data extraction, and general comfort working with AI tools and prompting.An understanding of point in time data is a strong plus Also desirable: experience with vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar); machine learning experience (PyTorch, TensorFlow, XGBoost/LightGBM) for building, maintaining and extending existing models; Bloomberg API integration to Python; data science/statistics, signal research, backtesting or factor modelling; full stack familiarity (Flask, Django, FastAPI); SaaS build/deploy on cloud (Docker, AWS or similar); an understanding of Java; and experience in quantitative finance or with financial market data. Experience: Bachelor's or Master's in Computer Science, Data Science, IT, Electrical Engineering or equivalent experience is essentialExperience working in a regulated financial services industry, preferable but not essentialProactive approach towards identifying opportunities for improvement and taking ownership to deliver solutions