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SlyceData

About us

Who are we?

Investment managers across the globe are struggling to integrate and utilize multiple data sources in their investment research process. This data wrangling is crippling investment research efforts to generate alpha - an unacceptable state in today’s competitive marketplace. 

We are experts in automating data management, programmatically generating real-time data queries, and providing intuitive tools for data access for portfolio managers, researchers, analysts, and data scientists. With accurate and efficient data access, your team can spend drastically less time on data management, and much more time on revenue-generating work. 

Our staff of data engineers and investment practitioners have spent the last 8 years building a technology platform that provides researchers with access to a normalized, concordant version of their disparate data sources - meaning from any vendor, instrument, or asset class, whether the data is externally sourced or internally derived. Your investment team can explore and extract their desired data using our intuitive tools, including a comprehensive data catalog, or our API functionality. This data is then delivered neatly packaged into any analytic endpoint, such as Jupiter Notebooks, BI tools, data virtualization platforms, or into our own built-in analytic environment. This drastically increases workflow efficiency. For example, one major asset manager improved processing time from 8 hours to 13 minutes for a dense data analytics request saving over $400k per year.

Designing technology to ‘automate’ this process is extremely difficult. It requires uniting two areas of expertise - functional programming expertise to create an intelligent query engine, and financial data expertise to embed the business logic of the data into this engine. In essence, we designed an intelligent AI-powered engine that dynamically interprets a user’s data requests, generates optimized queries in real time, and applies the necessary math/logic to deliver ready to analyze data without any pre-processing requirements.  

  • haskell
  • Fully Remote
  • flexible working environment
  • supportive culture
  • stock options
  • Finance
  • Seed
  • People: 10-49
  • Founded: 2021
  • Red Bank, NJ, US

Technology

Software Stack

(7)
  • Haskell
  • Angular
  • React
  • PostgreSQL
  • Nix
  • snowflake
  • nginx

DevOps

(5)
  • Docker
  • GitHub
  • Terraform
  • grafana
  • prometheus

Infrastructure

(3)
  • Helm
  • Kubernetes
  • AWS

Tools

(3)
  • Slack
  • G-Suite
  • HubSpot

Our backend is written entirely in Haskell, including the interpreter for SlyceData's proprietary programming language, the API service and all the background automation.

The application data is stored in Postgres. We're heavy users of Postgres' feature set for performance and precise semantics for the behavior of a number of agents coordinating around Postgres.

The application also processes vendor data hosted on a number of database engines, such as Postgres, SQL Server and Snowflake.

We use stack for managing our Haskell dependencies during development, and Nix + haskell.nix + dockerTools for building our production images.

We're believers of investing in development infrastructure. The backend team maintains a CI service written in Haskell, automating custom workflows for supporting the development activities of the backend team and the other teams depending on the backend services. We also invest in maintaining rich REPL environments providing quick access to platform features during development.

We make an effort to build the code on simpler Haskell idioms as much as it makes sense, but we also don't shy away from using any advanced techniques whenever the benefits in safety, conciseness or performance justify their use.

We depend on a lot of excellent Haskell packages, but the subset in the following list should give an idea about the main architectural direction:

  • Most of our endpoints are implemented with scotty, but some others use servant
  • mtl for the monadic glue, but we don't try to glue everything with monads
  • We use plain record syntax for simple cases and lens for more nested structures occasionally powered by generic-lens
  • A lot of the performance-critical code makes heavy use of vector
  • Mostly postgresql-simple and postgresql-query for talking to Postgres
  • HDBC + HDBC-odbc along with a suite of utilities for talking to other DB engines over ODBC.
  • tasty for the unit and integrations tests along with QuickCheck
  • lensrank2classesreflection etc. when we really need to model complex relationships between types
  • happy + alex for parsing our DSL

We have a sizable body of code we'd like to contribute back to the community, but can't, due to time constraints, so we need your help to do that!

Testing

Manual
Fully automated

Ops

DevOps
Dedicated Ops team

Time to deploy

More than 5 hours
Less than 1 hour

Benefits

Culture

(2)
  • Remote Working
  • Flexible Schedules

Health & Wellness

(2)
  • Health Insurance
  • Dental / vision

Financial benefits

(2)
  • 401(K)
  • Stock Options

Parents

(1)
  • maternity & paternity leave

Vacation

(1)
  • Unlimited Vacation

Professional development

(1)
  • Promote From Within

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Jobs (1)

View all
Building an intelligent query engine to better connect financial datasets with quant investors
Remote
  • Finance
  • Seed
  • People: 10-49
  • Founded: 2021
  • Red Bank, NJ, US

Hire with us!

Create a free profile page for your company.

Use this space to connect with our community. Companies with profiles typically get 20% more applications!