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Senior Software Engineer - Risk Intelligence at Mercury

Job details

Ancient Roman roads, extending over 250,000 miles, were crucial for the swift movement of armies and trade, helping to unify and control the vast empire. Their advanced design ensured efficient resource distribution and communication. Similarly, modern data engineering provides the infrastructure for today's digital landscape, creating seamless data pipelines and storage systems. 

Mercury has a quickly growing digital footprint, with a variety of teams, problems, and information that needs connecting. Just as Roman roads connected and empowered the region, the Risk Intelligence team works to connect diverse technical teams, enabling informed decisions and driving success across the fraud, risk, and compliance spaces.

In this position you will lead the design and development of sophisticated data systems tailored to ensure regulatory compliance and detect potential fraud on the platform. As an engineer experienced in modern data architecture you will help mentor and teach the team best practices for technical design. You will help us understand data technologies, both new and mature, and how to wield them properly. You will bring a set of opinions forged in the experience of crafting similar works and let your curiosity and love of learning guide you carefully forward.

As part of this role, you will:

  • Join a compassionate team of experienced Haskell engineers
  • Feel a strong sense of ownership and actively seek responsibility – we often self-organize on small/medium projects, and we want someone who’s excited to help shape and build Mercury’s future.
  • Design, build, and evolve a burgeoning feature platform system
  • Carefully maintain and grow our existing products and data solutions
  • Carefully direct data domain modeling, (re)designing tables and doing deep query optimizations
  • Work independently, asynchronously, and remotely. Developing habits and principles to help uphold this style of team operation.
  • Work closely with product engineers, data analysts, data scientists and product leaders to ship lovable and scalable experiences.

The ideal candidate for the role:

  • Has experience designing and building large data systems, pipelines, and ETL systems. Event-driven design experience is a plus.
  • Has at least 7 years of development experience, with excitement to learn and work with Haskell, React, and TypeScript.  Comfort coming up to speed quickly on programming languages, libraries, and mature code bases is essential.
  • Is passionately product minded and has experience building and shipping new products.
  • Empathizes with users and can channel their perspective to build lovable products.
  • Can balance tradeoffs and find creative solutions to accomplish goals.
  • Communicates and collaborates effectively with peers and cross functional counterparts.

We encourage you to try our demo site if you’re interested in applying for the role.

The total rewards package at Mercury includes base salary, equity (stock options), and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.

Our target new hire base salary ranges for this role are the following:

  • US employees (any location): $203,100 - $238,900
  • Canadian employees (any location): CAD 184,800 - 217,400

*Mercury is a financial technology company, not a bank. Banking services provided by Choice Financial Group and Evolve Bank & Trust, Members FDIC.

 

We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024. Please see the independent bias audit report covering our use of Covey here.

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