Senior Data Engineer

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most, starting with their homes and expanding to all they value, in a world where climate risks, rising costs, and outdated systems leave too many behind.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.

So What’s the Role?

As a Senior Data Engineer, you will play a pivotal role in ensuring the quality, integrity, and security of Kin’s data assets. You’ll be responsible for building out a data lakehouse for enterprise reporting, which will involve the design and development of production-ready data pipelines, modeling raw data for downstream reporting, and quality assurance of the data warehouse. 

What You’ll Do

You’ll work on a cross-functional project team with members from AppEng, DataEng, BI, and Kin’s business   to design solutions  for effective reporting. You’ll implement data validation standards and QA procedures to ensure effective data lifecycle and management practices.  This involves:

• Designing and developing scalable data pipelines and models for downstream analytics and reporting

• Leading and collaborating with a cross-functional project team to implement data validation, QA standards, and effective data lifecycle management

• Optimizing pipeline performance, cost, and data quality in a large-scale data environment

• Migrating data warehouse (DBT, Redshift) architecture to Lakehouse (e.g: S3, Glue, Databricks, Unity catalog) architecture

• Mentoring data engineers and promoting best practices in software engineering, documentation, and metadata management

• Ensuring data security and compliance with regulations (e.g., GDPR, CCPA, GLBA) through robust pipeline design and access monitoring

• Translating ambiguous business requirements into technical solutions using marketing domain knowledge
  

The skills to succeed

• 4+ years of hands-on data engineering experience related to:

  • 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, Kafka, Hadoop, or similar)

• Excellent communication skills and ability to explain complex concepts clearly and concisely

• Detail-oriented with strong data intuition and a passion for data quality

• Proven ability to model data and build production-ready ETL pipelines handling TBs of data.

• Great time management and prioritization skills
  

You should join us if…

• You want to work on cutting-edge data solutions in a high-impact, regulated industry

• You enjoy collaborating with talented teams to solve complex problems and drive business outcomes

• You want to learn from and mentor team members in a dynamic, growth-oriented environment with opportunities to innovate

• You value making a real world impact on people’s lives through the use of data and technology

• You’re outcome oriented, as well collaborative. Taking ownership and prioritizing key outcomes is important, but so is taking an emotionally intelligent approach to working with others.
  

You probably shouldn’t apply if…

• You are uncomfortable with ambiguity or unclear requirements. We solve a lot of new problems where requirements are not well defined. 

• You would rather avoid start-up environments where processes may not be well-defined. Change is a way of life at Kin. We’re still establishing some of our processes and evolving how we manage data. 

• You see perfection as the only option. We move fast, and we make mistakes. We learn