Machine Learning Engineer
Goosehead Insurance
Data Engineer
Design, build, and maintain scalable ETL/ELT pipelines to support analytics and machine learning workloads.
Develop and manage robust data models and warehouse structures that support self-service analytics and reporting.
Work with stakeholders across the business to understand data requirements and ensure data availability, accuracy, and usability.
Implement and monitor data quality and validation checks to maintain trust in our data assets.
Collaborate closely with data science and analytics teams to ensure data infrastructure supports model training and deployment.
Optimize data storage and query performance across cloud-based and relational systems.
Stay current with emerging data engineering tools and architectures and advocate for best practices across the team.
5+ years of experience in data engineering, data infrastructure, or related fields.
Proficiency in SQL and at least one programming language (e.g., Python, Java, Scala).
Experience working with cloud data platforms (e.g., AWS Redshift, Snowflake, BigQuery).
Strong knowledge of data modeling, data warehousing, and building ETL/ELT pipelines.
Familiarity with modern data orchestration tools (e.g., Airflow, dbt).
Excellent communication and collaboration skills.
Experience with real-time data streaming technologies (e.g., Kafka, Kinesis).
Familiarity with CI/CD for data pipelines and infrastructure-as-code (e.g., Terraform).
Experience supporting machine learning workflows and model deployment.
Background in insurance, financial services, or other highly regulated industries.