Senior Data Engineer at Kin

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

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:

In your first 6–12 months at Kin, success is less about checking boxes and more about the impact you create. You’ll use your skills and judgment to take ownership of meaningful work, improve how we operate, and help move Kin’s mission forward.

By the end of your first year, you should feel confident in your role, trusted as an owner, and proud of the progress you’ve helped make.

• 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) 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