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Senior Risk Analytics Engineer

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Pipe is building cutting edge embedded financial solutions to help small businesses and software companies build something bigger. We’re a remote-first team of problem solvers who build and ship at high velocity. We’re customer obsessed and leave our egos at the door. If this sounds fun, join us!

Role

We are hiring a Senior Analytics Engineer to join our Risk team. This role will focus heavily on data modeling to support our risk business analysts, who are responsible for using dashboards to regularly analyze and report on credit risk and underwriting to the business. You will design and develop data models using SQLMesh to support risk business analysts, create reports, construct dashboards, evaluate key metrics, and perform ad hoc analysis using SQL or Python. You will collaborate closely with the Data Science and Data Engineering teams to ensure our data infrastructure is as self-service as possible.

Currently our data stack is mostly comprised of:

Data Analytics: Preset, Hex
Data Warehouse: Google BigQuery
Data Modeling: SQLMesh
MLOps: Chalk, Metaflow

We do not expect you to have experience with all of these tools. Experience with similar tools like Tableau, Amazon Redshift, or dbt are good indicators that you’d be comfortable with our stack.

Pipe is a remote company with hubs in SF, Atlanta, and New York. We have large scale challenges to tackle in multiple areas, and we invest heavily in the developer experience. We believe great engineers are capable of learning new things, and you will receive mentorship and guidance as needed.

Responsibilities

Collecting and integrating data from various sources such as credit bureaus, banks, payment systems, and internal financial systems.
Designing and developing efficient data pipelines that move and process credit-related data from various sources into BigQuery.
Working closely with credit analysts and data scientists to provide the necessary data for evaluating credit risk, creating credit scores, and assessing financial trends.
Automating data processing tasks and improving the efficiency of data pipelines, reducing manual effort and time required for data preparation.
Collaborating with data engineering and software engineering teams to understand and implement best practices for data modeling.

Qualifications

We are looking for data analysts with this kind of background:

3-5+ years of experience writing well-structured, performant SQL and advanced Python
3+ years of experience in database schema design and data modeling
3+ years of experience building data models and data pipelines on top of large datasets
Track record of following data modeling standards and best practices
Proficiency in common engineering tools such as Git, Jira, Unix-based operating systems, etc.
Experience with common data visualization tools such as Tableau, Superset, or Looker
Passionate about extracting and communicating insights to support the business
Prefer familiarity with concepts such as decision models, features, portfolio performance
Experience in an agile team and collaborating asynchronously
Curious and eager to learn

Compensation and Benefits

We believe in taking care of our employees. We want you to feel like an owner and that will be reflected in your salary, equity, and benefits. You’ll receive:

The best equipment to help you do your job: computers, monitors, desks, chairs, headphones, speakers, webcams, keyboards, mice, etc.
Flexible vacation and work hours. We believe in a healthy work-life balance (really!)
Excellent health, dental, and vision insurance.
Generous parental leave for anyone who is growing their family, regardless of gender.
Great colleagues! We value a culture of authenticity, humility, and excellence. We want you to make a mark on our culture.
Lifetime maximum benefit for family forming and fertility.

Pipe is an equal opportunity employer: we do not discriminate. Diversity and inclusion are important to us, and we hope they are to you, too.

The annual US base salary range for this role is $113,000 - $155,000. This salary range may be inclusive of several career levels at Pipe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location.

Average salary estimate

$134000 / YEARLY (est.)

min

max

$113000K

$155000K

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What You Should Know About Senior Risk Analytics Engineer, Pipe Technologies

Join Pipe as a Senior Risk Analytics Engineer, where we are passionate about creating cutting-edge embedded financial solutions that empower small businesses and software companies to scale. As part of our dynamic and remote-first team, you'll play a pivotal role in supporting our risk business analysts through data modeling. Your primary focus will be to design and develop crucial data models using SQLMesh, facilitating the regular analysis of credit risk and underwriting. You'll be an essential collaborator with data scientists and engineers, making our data infrastructure as self-service as possible. We utilize a data stack that includes Google BigQuery, Preset, Hex, and more, although you don’t need to have direct experience with every tool. Your responsibilities will range from collecting and integrating data from diverse sources, like credit bureaus and payment systems, to automating data processes for efficiency. With a commitment to mentorship and learning, we welcome you to tackle large-scale challenges in a culture that highly values developer experience. If you're ready to leave egos at the door and be part of a customer-obsessed team, we’d love to hear from you!

Frequently Asked Questions (FAQs) for Senior Risk Analytics Engineer Role at Pipe Technologies

What are the key responsibilities of a Senior Risk Analytics Engineer at Pipe?

As a Senior Risk Analytics Engineer at Pipe, your key responsibilities include collecting and integrating data from various sources, designing and developing efficient data pipelines, working closely with credit analysts and data scientists, automating data processing tasks, and collaborating with engineering teams to implement best practices in data modeling.