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Kin Insurance is seeking a Senior Data Engineer to design and build scalable, production-grade data pipelines and models that power enterprise reporting and decision-making across the organization. You'll own the modeling layer between raw source data and downstream consumers—analytics engineers, BI teams, and business stakeholders in Finance, Marketing, and Product.
In this role, you will:
- Design and implement scalable data pipelines and dimensional models for analytics and enterprise reporting
- Establish and enforce data validation, testing, and QA standards to ensure accuracy and reliability
- Partner with App Engineering to understand source systems and define how raw data should be captured and modeled
- Collaborate with Analytics Engineering, BI, and business stakeholders to translate reporting requirements into well-modeled datasets
- Ensure data models comply with security and privacy regulations (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 to improve engineering efficiency and code quality
Kin is a remote-first, venture-backed insurtech founded in 2016, focused on making homeowners insurance simpler and more affordable. The company has achieved strong growth, profitability, and customer satisfaction, with recognition from Built In Chicago, Forbes, and Great Places to Work.
Success in your first 6–12 months means delivering well-modeled, production-ready datasets that become the default source of truth for reporting, reducing one-off requests and rework. You'll establish data quality standards that measurably improve stakeholder trust and enable cross-functional partners to make faster, better-informed decisions.
REQUIREMENTS:
- 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 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 ability to explain complex technical concepts to non-technical stakeholders
