AlphaSense is looking for a Software Engineer to join the Reference and Entity Data Services mission to build systems that ingest, normalize, deduplicate, and publish public and private company data to all AlphaSense services that surface company information, ensuring data quality, coverage, and timeliness for millions of companies worldwide.
Requirements
- Python, MySQL, PostgreSQL, ClickHouse and AWS
- 3+ years of software development experience building highly reliable, mission-critical software
- Strong software engineering skills in back-end engineering on data-intensive applications with professional, real-world experience with applications at scale
- A strong foundation in computer science fundamentals, such as algorithmic complexity, asynchronicity, and distributed computing
- Experience writing high-quality code and tests, which includes handling error cases, asynchronous code, streaming data, caching, logging and analytics for understanding behavior in production
- Knowledge of modern development practices, including CI/CD pipelines and automated testing frameworks
Responsibilities
- implement new data feed ingestions
- integrate reference data with downstream systems
- ensure the health of systems that manage large flows of strategically important data
- Test, review and deploy code quickly
- Research, learn and share new techniques for solving complex engineering problems
- Write tools and develop practices for the engineering team
- building a system to measure search engine performance, reworking an integration with a third-party provider, or designing the next generation of a data pipeline
Other
- Excellent communication, organizational, problem-solving, debugging, and analytical skills
- Ability and desire to work in an open and team-oriented environment
- We deploy over 10 times per day and manage major releases with feature flags rather than coordinated deployments
- We’re a highly collaborative team and we push each other to find better solutions every day
- From GraphQL-to-Typescript compilers to fully automated deployments, we take the time to invest heavily in our own productivity
