Senior Data Engineer
New
K
KinInsurtech
For all other positions, these roles can sit in any of the following 41 states: AL, AR, AZ, CA (exempt only), CO, CT, FL, GA, ID, IL, IN, IA, KS, KY, LA, MA, ME, MD, MI, MN, MO, MT, NC, NE, NJ, NM, NV, NY, OH, OK, OR, PA, SC, SD, TN, TX, UT, VT, VA, WA, and WI... For remote technical positions located in Canada, we are only able to hire individuals who reside in Ontario.Full-TimeSenior
Salary$131,950 - $153,026
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonSQLNumpySnowflakePandasRedshiftDatabricks
Requirements
- 4+ years of experience in data engineering, analytics engineering, or dimensional modeling roles, building production data models
- Advanced SQL skills, with experience transforming data from multiple sources into a scalable warehouse or lakehouse
- Proficiency in Python (Pandas, NumPy, etc.) for data transformation and pipeline development
- Expertise in dimensional modeling, ELT workflows, and modern data architecture patterns
- Proven ability to model raw, complex data into well-structured, analytics-ready datasets
- Experience working with platforms such as Databricks, Snowflake, or Redshift
- Ability to translate ambiguous business requirements into scalable, well-modeled datasets
- Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
Responsibilities
- Design and build scalable, production-grade data pipelines and data models to power downstream analytics and enterprise reporting
- Implement and enforce data validation, testing, and QA standards across data models to ensure accuracy and reliability
- Partner with App Engineering to understand source systems and define how raw data should be captured and modeled downstream
- Collaborate with Analytics Engineering, BI, and business stakeholders to translate ambiguous reporting needs into scalable, well-modeled datasets
- Ensure data models comply with data security and privacy regulations through access controls and monitoring
- Mentor other data engineers on modeling best practices, documentation, and data processing patterns
- Leverage AI-assisted development tools where appropriate to improve engineering efficiency, code quality, and observability
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