[Hiring] Data Engineering Manager @Kin Insurance

Role Description

Lead a team of data engineers building scalable pipelines and platforms that power analytics, data science, and business decisions across Kin.

We’re looking for a Data Engineering Manager to lead and evolve the systems that power data across Kin. This role exists to strengthen the foundation that enables Business Intelligence, Data Science, and Machine Learning teams to move faster, make better decisions, and deliver meaningful impact to our customers.

You’ll lead a team of data engineers responsible for building reliable, scalable, and well-governed data systems. At a moment of continued growth and increasing data complexity, you’ll help define how we scale our data platform while balancing immediate business needs with long-term technical investments.

Your responsibilities

• Lead and manage a team of ~6 data engineers, driving execution, performance, and career development
• Own Kin’s data platform, including ingestion, storage, transformation, pipeline orchestration, and governance
• Define and execute on a Data Engineering strategy that supports analytics, data science, and product use cases
• Build and optimize scalable data pipelines and architectures using tools like Snowflake, Databricks, DBT, and Airflow
• Partner closely with Engineering, Product, BI, and Data Science to deliver high-impact data solutions
• Establish best practices for data reliability, testing, CI/CD, and observability across pipelines
• Guide the team through large, complex initiatives with iterative delivery and clear prioritization
• Drive improvements in data quality, accessibility, and documentation across the organization
• Foster a culture of ownership, accountability, and continuous improvement within the team

What success looks like

• The data platform is more reliable, scalable, and easier for stakeholders to use across the business
• Data pipelines are well-tested, observable, and consistently meet performance and freshness expectations
• Cross-functional teams (BI, Data Science, Product) are unblocked and able to move faster with trusted data
• The Data Engineering team is operating efficiently, with clear priorities, strong ownership, and high engagement

Qualifications

• Experience leading and managing a data engineering team, ideally in a high-growth or startup environment
• Hands-on expertise with modern data platforms such as Snowflake and/or Databricks
• Experience building and maintaining data pipelines using tools like DBT, Airflow, DMS, or Airbyte
• Familiarity with streaming data technologies such as Kafka or Flink
• Strong background in large-scale data architecture and data modeling
• Experience implementing engineering best practices including CI/CD, automated testing, and monitoring
• Proficiency in Python for data engineering use cases

Bonus

• Exposure to AI/ML workflows or predictive analytics
• Experience with infrastructure as code tools such as Terraform

Benefits

• Competitive salary and company equity through Restricted Stock Units (RSUs), granted as part of our standard compensation package
• 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
• Employee referral bonuses
• A variety of supplemental benefit options, 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, including up to 14 weeks at 100% pay for birthing parents and 8 weeks at 100% pay for non-birthing parents
• Career mobility and internal growth opportunities across the organization
• Professional development budgets for certifications, conferences, and learning available, subject to management approval