Data Engineering Manager

Kin Insurance

1d•Remote

About The Position

Lead the evolution of Kin’s modern data platform—owning scalable pipelines, reliable lakehouse infrastructure, and the systems that power analytics, AI, and operational decision-making across the business. We’re looking for a Data Engineering Manager to help scale the data platform that powers Kin’s analytics, operational decision-making, and machine learning capabilities. This role sits at the center of how data moves through the company—ensuring teams across engineering, product, analytics, and data science can trust and use data reliably at scale. As Kin continues to grow, the complexity and importance of our data ecosystem is growing with it. You’ll lead the evolution of our lakehouse architecture, ingestion pipelines, governance tooling, and platform reliability practices while managing and developing a team of data engineers. This is an opportunity to shape the long-term direction of data engineering at a company where data is deeply connected to customer experience, operational efficiency, and business performance. You’ll work closely with analytics engineering, data science, application engineering, security, and compliance partners to create scalable systems and operational patterns that make trusted data accessible across the organization.

Requirements

6–8+ years of experience in data engineering, platform engineering, or infrastructure-focused data roles, including experience managing and developing data engineering teams
Hands-on experience building and operating modern data platforms using technologies such as Databricks, Snowflake, BigQuery, or similar lakehouse and warehouse ecosystems
Experience designing and maintaining scalable batch, streaming, and event-driven data pipelines using tools such as PySpark, SQLMesh, Kafka, Fivetran, or Databricks Jobs
Experience implementing CI/CD, automated testing, monitoring, and observability practices for data infrastructure and pipelines
Experience defining and implementing data governance, privacy, classification, retention, and access control frameworks in regulated or compliance-sensitive environments
Proficiency with Python and PySpark for large-scale data engineering use cases
Experience managing infrastructure-as-code and GitOps workflows using tools such as Terraform
Ability to partner effectively across technical and non-technical teams while navigating ambiguity, operational tradeoffs, and competing priorities
Experience leading teams through complex platform migrations, architectural evolution, or operational scaling initiatives

Nice To Haves

Exposure to AI/ML workflows, predictive analytics systems, or MLOps integration patterns
Experience operating in organizations where data engineering and analytics engineering are distinct but closely aligned functions

Responsibilities

Lead and develop a team of data engineers, driving execution, operational excellence, and long-term growth of the data engineering discipline
Own the reliability, scalability, and evolution of Kin’s data platform infrastructure and lakehouse environment within Databricks
Design and optimize batch, streaming, and event-driven data pipelines using technologies such as PySpark, Databricks, SQLMesh, Kafka, and Fivetran
Establish engineering standards for testing, observability, CI/CD, monitoring, incident response, and operational reliability across the data platform
Define and implement governance, lineage, classification, retention, and access control patterns in partnership with Security, Legal, and Compliance teams
Drive infrastructure-as-code and GitOps practices for platform resources using tools such as Terraform
Partner with application engineering teams to build resilient integrations between source systems and the data platform while proactively managing schema and dependency changes
Collaborate with analytics engineering, product, and data science stakeholders to align platform investments with business priorities and downstream data needs
Guide the team through large-scale technical initiatives with clear prioritization, iterative delivery, and thoughtful operational tradeoff decisions
Identify opportunities to improve engineering efficiency and platform scalability through automation, tooling improvements, and thoughtful use of AI-enabled workflows where appropriate

Benefits

Competitive salary and company equity through Restricted Stock Units (RSUs), granted as part of our standard compensation package and based on role and level
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
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