Job Description

Position Overview

• Location Type: Remote
• Job Type: FullTime
• Salary: $125K - $153K

Kin is redesigning insurance to be smarter, faster, and centered on the customer. We use intelligent pricing, offer seamless bundling, and make every step (from purchasing, servicing to claims) simple and friction-free, especially in the places traditional insurers often ignore. We empower people to protect what matters most, starting with their homes and expanding to all they value, in a world where climate risks, rising costs, and outdated systems leave too many behind.

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 (2021-2025)
• Forbes’ America’s Best Startup Employers (2021- 2024, Ranked #37 in 2024)
• 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.

Requirements

• Experience: 4+ years of hands-on data engineering experience
• Technical Skills:
  • Data structures and cloud platform environments and best practices (AWS strongly preferred, Azure, or GCP)
  • ETL performance tuning and cost optimization
  • Data lake and lakehouse patterns including open table formats (e.g. Iceberg, Hudi, Delta)
  • Proficiency in Python (Pandas, NumPy, etc.) and SQL for advanced data processing and querying
  • Expertise in distributed data processing/storage (e.g., Apache Spark)

Responsibilities

• Pipeline Development: Designing and developing scalable data pipelines and models for downstream analytics and reporting
• Collaboration & QA: Leading and collaborating with a cross-functional project team to implement data validation, QA standards, and effective data lifecycle management
• Optimization: Optimizing pipeline performance, cost, and data quality in a large-scale data environment
• Architecture Migration: Migrating data warehouse (DBT, Redshift) architecture to Lakehouse (e.g: S3, Glue, Databricks, Unity catalog) architecture
• Mentorship: Mentoring data engineers and promoting best practices in software engineering, documentation, and metadata management
• Compliance & Security: Ensuring data security and compliance with regulations (e.g., GDPR, CCPA, GLBA) through robust pipeline design and access monitoring
• Requirements Translation: Translating ambiguous business requirements into technical solutions using marketing domain knowledge