Senior Data Engineer

Kemper is one of the nation’s leading specialized insurers, and they are seeking a highly skilled Senior Data Engineer to guide the design, development, and delivery of their enterprise data platforms. This role involves providing technical leadership, architecting scalable data solutions, and ensuring the operational excellence of data systems.

Responsibilities:

• Provide technical leadership to data engineers, setting standards for solution design, coding practices, data governance, and quality
• Define and evolve the enterprise data architecture leveraging Snowflake, Data Vault 2.0, and modern event driven and ELT frameworks
• Lead architecture reviews, establish engineering best practices, and guide platform modernization efforts
• Mentor engineering team members, facilitate code reviews, and promote continuous learning and innovation
• Architect and oversee the delivery of scalable data pipelines, ingestion frameworks, and transformation processes using Snowflake, Python, Spark, Informatica (PowerCenter/IDMC), AWS Glue, and cloud-native tooling
• Design and maintain enterprise data models, including Data Vault 2.0 (Hubs, Links, Satellites, PIT, Bridge structures) and dimensional models
• Develop and optimize Snowflake platform capabilities, including:
• Snowpipe, Streams, Tasks, File Formats, External Stages
• Dynamic Tables and ELT pipeline automation
• Warehouse sizing, performance tuning, and cost optimization
• Implement scalable batch, micro-batch, and real-time ingestion solutions following best practices
• Translate complex business requirements into technical design specifications and actionable development plans
• Partner with product owners, business stakeholders, architects, and analytics teams to deliver high-impact data solutions
• Manage technical execution across multiple initiatives, ensuring alignment with enterprise priorities, data strategies, and delivery timelines
• Oversee documentation, deployment readiness, testing processes, and quality assurance for production releases
• Ensure operational reliability, data accuracy, and performance of enterprise data warehouse and analytical environments
• Lead root-cause analysis for production issues and drive implementation of long-term preventative solutions
• Implement robust monitoring frameworks using tools such as Snowflake Resource Monitors, CloudWatch, Datadog, or equivalent observability platforms
• Conduct performance optimization on Snowflake workloads, SQL queries, pipelines, and integrations

Requirements:

• 8+ years of experience in data engineering, with 2+ years in a senior, lead, or architect capacity
• Advanced hands-on expertise in Snowflake, including constant learning on advanced new features
• Strong experience implementing Data Vault 2.0 models and automated ELT frameworks
• Proficiency with ETL/ELT and data integration tools such as Informatica IDMC, PowerCenter, Pentaho, AWS Glue, Control M and Python-based pipelines
• Deep understanding of: Data warehousing and analytics engineering principles, Dimensional modeling and relational structures, ELT patterns, CDC frameworks, and metadata-driven design, Orchestration frameworks (Control-M, Airflow)
• Strong SQL expertise with experience in stored procedures, Snowflake Scripting, and complex query optimization
• Hands-on experience designing and delivering large-scale AWS-based data platforms
• Demonstrated leadership in guiding teams, influencing architecture decisions, and managing technical delivery
• Excellent communication and collaboration skills with the ability to partner across technical and business teams
• Experience with advanced Snowflake features: Snowpark (Python/Java/Scala), Snowflake Warehouses, Databases, Schemas, RBAC, Snowpipe, Tasks, Streams, Stages, File Formats, Performance tuning, warehouse optimization, clustering, Data sharing, secure data sharing, reader accounts, AI/ML Function
• Experience with modern DataOps, DevOps, and automation frameworks: CI/CD (GitHub Actions, GitLab CI, Azure DevOps), Infrastructure-as-Code (Terraform, CloudFormation), Automated testing frameworks for data pipelines
• Familiarity with data quality and governance tools such as Great Expectations, Monte Carlo, Datafold, Soda, Alation, or Collibra
• Experience with streaming technologies such as Kafka, Kinesis, or MSK
• Exposure to ML feature engineering pipelines or MLOps practices
• P& C Insurance industry experience, specifically auto insurance experience required and is strongly preferred
• Prior experience on data warehousing with Guidewire PolicyCenter, ClaimCenter or BillingCenter and Agency Licensing data is preferred
• Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent relevant experience/certifications