Senior Data Engineer

Kemper offers a collaborative, growth‑oriented environment with a strong focus on data modernization. This Senior Data Engineer role leads the design, development, and delivery of enterprise data platforms leveraging Snowflake and AWS, enabling analytics, reporting, and business intelligence across the organization. You will benefit from an annual discretionary bonus, comprehensive medical, dental, and vision coverage, PTO, and a 401(k) plan. The position is based in Charlotte, North Carolina with a hybrid work arrangement.

Location

Charlotte, North Carolina — hybrid work arrangement.

Why join us

• Opportunity to lead the technical direction of an enterprise‑wide data platform.
• Work with modern cloud‑native technologies and shape the organization’s data strategy.
• Collaborative, growth‑oriented environment with strong investment in data modernization.
• Sponsorship is not available for this opportunity.
• This position can be worked hybrid out of a local Kemper office, including Chicago, IL, Birmingham, AL, Richardson, TX, or Jacksonville, FL. For non‑local candidates, remote options with travel are available.

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 components 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 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 with ongoing learning of advanced 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).

Technologies

• Snowflake, Data Vault 2.0, Python, Spark
• Informatica PowerCenter, Informatica IDMC, AWS Glue
• Snowpipe, Streams, Tasks, File Formats, External Stages
• Snowflake Scripting, Snowpark, Control‑M, Airflow
• Pentaho, Kafka, Kinesis, MSK
• Terraform, CloudFormation, GitHub Actions, GitLab CI, Azure DevOps
• Great Expectations, Monte Carlo, Datafold, Soda, Alation, Collibra
• ML feature engineering pipelines, MLOps

Benefits

• Annual discretionary bonus
• Medical insurance
• Dental insurance
• Vision insurance
• Paid time off (PTO)
• 401(k) plan