Senior Data Engineer - Kin

Quick summary

Build Kin’s next-generation lakehouse architecture, design scalable pipelines, and ensure trusted, secure data for enterprise reporting in a fast-growing insurtech.

Who we are

Kin makes life simpler, more affordable, and better for homeowners — especially in the places where climate risks, rising costs, and outdated systems make it harder. We start with smarter homeowners insurance and expand to everything homeowners need to thrive.

Using data, technology, and thoughtful human support, we’re building products that are clear, fair, and help homeowners feel confident — so homeowners aren’t left behind when they need help most.

Founded in 2016, Kin is a remote-first employer with Kinfolk across more than 35 states. We serve customers in 13 states (and counting). Our disciplined growth, strong customer satisfaction, and focus on long-term sustainability fosters outstanding growth, attracts marquee investors, and earns industry recognition.

The opportunity

We're looking for a Senior Data Engineer to turn raw, complex data into trusted, well-modeled datasets that power reporting and decision-making across Kin. As we grow, the accuracy and structure of the data underneath our reporting matters more every quarter — teams can only move as fast as the data they're standing on.

You'll own the modeling layer between raw source data and the people who depend on it: analytics engineers, BI, and business stakeholders across Finance, Marketing, and Product. You'll work closely with App Engineering to understand source systems and with downstream teams to make sure what you build actually answers their questions.

Your responsibilities

• Design and build scalable, production-grade data pipelines and data models to power downstream analytics and enterprise reporting
• Implement and enforce data validation, testing, and QA standards across data models to ensure accuracy and reliability
• Partner with App Engineering to understand source systems and define how raw data should be captured and modeled downstream
• Collaborate with Analytics Engineering, BI, and business stakeholders to translate ambiguous reporting needs into scalable, well-modeled datasets
• Ensure data models comply with data security and privacy regulations (e.g., GDPR, CCPA, GLBA) through access controls and monitoring
• Mentor other data engineers on modeling best practices, documentation, and data processing patterns
• Leverage AI-assisted development tools where appropriate to improve engineering efficiency, code quality, and observability

Success in this role

• Well-modeled, production-ready datasets are the default source of truth for reporting, cutting down on one-off requests and rework
• Data validation and QA standards are consistently applied, resulting in measurable improvements in data quality and stakeholder trust
• Cross-functional partners rely on your models to make faster, better-informed decisions

What you’ll bring

• 4+ years of experience in data engineering, analytics engineering, or dimensional modeling roles, building production data models
• Advanced SQL skills, with experience transforming data from multiple sources into a scalable warehouse or lakehouse
• Proficiency in Python (Pandas, NumPy, etc.) for data transformation and pipeline development
• Expertise in dimensional modeling, ELT workflows, and modern data architecture patterns
• Proven ability to model raw, complex data into well-structured, analytics-ready datasets
• Experience working with platforms such as Databricks, Snowflake, or Redshift
• Ability to translate ambiguous business requirements into scalable, well-modeled datasets
• Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders

How we support you

• Competitive salary and company equity through Restricted Stock Units (RSUs)
• 401(k) with company match up to 4% of eligible earnings
• Multiple medical plan options, plus dental and vision coverage
• Company-funded HSA contributions (based on medical plan selection)
• Company-paid life insurance and short-term disability
• Supplemental benefits including long-term disability, critical illness, accident, legal, and pet insurance
• Access to mental health support and confidential counseling resources
• Flexible PTO for exempt employees (most employees take 15–20 days per year), plus 8 company-observed holidays
• Paid parental leave (up to 14 weeks at 100% pay for birthing parents, 8 weeks at 100% pay for non-birthing parents)
• Career mobility and internal growth opportunities
• Professional development budgets for certifications, conferences, and continuous learning