Staff Data Engineer - Insurance Data

Kin is on a mission to change home insurance from what it is to what it should be. Whether we’re leveraging data to create customizable coverage or providing claims service that goes above and beyond expectations, our members are at the heart of everything we do. In the face of ever-growing climate risk, they deserve an insurance company that cares about them. We aim to stick with our members through thick and thin.

We use efficient technology that lets homeowners buy directly from us to keep costs down. This is the essence of Kin. Our approach has fostered amazing growth, attracted marquee investors, and earned us accolades, including being named to:

• Built In Chicago's Best Places to Work, Midsize Companies (2021-2024).

• Forbes' America's Best Startup Employers (2021- 2023).

• Inc. 5000 Fastest-Growing Private Companies.

• Forbes’ Fintech 50.

Simply put, our people are what make us great – we need forward-thinking, inspired game-changers like you to join us in our mission.

So, what’s the role?

Currently our data engineering organization is building data lakehouses in Databricks that will be used for enterprise reporting. Data Engineering teams are split into two general focus areas–growth data and insurance data.

As a Staff Data Engineer, you will be the technical lead within a team of data engineers focused on insurance data related to claims, policies, products, rates, pricing, renewals, risk, and more.

You will be responsible for designing and developing data pipelines and modeling raw data for downstream ingestion. You’ll play a pivotal role in ensuring the quality, integrity, and security of our data assets.  You’ll also be responsible for mentoring and guiding data engineers on your team and across the organization, while collaborating with other engineers, product managers, analysts, and stakeholders. 

You will also lead a cross-functional project team with members from AppEng, DataEng, BI, and business stakeholders. This team is tasked with designing solutions to produce effective reporting, implementing data validation standards, and QA procedures to ensure effective data lifecycle and management practices.

Are we a fit for one another?

We recognize that not everyone will be a fit for us, and we’re not a fit for every data engineering professional. Here are some things to help you decide:

• You have a depth of experience in modern big data environments. More than just cloud-based data experience, you’ve worked in a lakehouse environment and built end to end data solutions with modern tools. At this level, advanced knowledge and experience with SQL and Python is expected. The same goes for database design. The thing that will really impress us is your ability to set up jobs and notebooks and teach other engineers how to use these more advanced tools.

• You have insurance domain knowledge. You’re not just following Jira stories and mapping data to existing models; you’re designing the models and writing the schemas, often with some ambiguous specs. Your problem solving skills and insurance knowledge will be essential to setting your team and downstream stakeholders up for success. 

• You like connecting your technical work to business goals and teams. You establish relationships and trust with stakeholders to better define technical requirements, and ensure your team’s work aligns with business goals.

• You prioritize data quality and integrity to optimize performance. Assessing the effectiveness and accuracy of data sources and data gathering techniques, analyzing database configurations, and evangelizing iterative testing practices is second nature.

• You value documentation. Maintaining a centralized metadata catalog documenting data assets, schemas and lineage is a standard practice for you.

• You want to work for a company that prioritizes security and compliance. We operate in a highly regulated industry, so we have high standards for data security. You’re someone who will ensure that pipelines and storage solutions comply with Kin’s standards on privacy regulations such as GDPR, CCPA, and GLBA, as well as monitor access logs and audit trails to detect unauthorized data access or usage.

You probably shouldn’t apply if:

• You prefer to have clearly defined requirements and previously established solutions to iterate or build upon. 

• You only want to do the most exciting or strategic work. Our teams are collaborative, and we believe in running through walls together. The work is hard and everyone contributes to hands-on problem solving, including leads.

• You haven’t led or don’t want to lead teams and projects. While you won’t have direct reports, you will be a pivotal leader within our data org, and that comes with great responsibil