Senior Data Engineer
| Company | Goosehead Insurance |
|---|---|
| Location | Lakewood, CO, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Senior |
Requirements
- Experience in SQL optimization and performance tuning, and development experience in programming languages like Python, PySpark, Scala etc.
- Experience working with cloud data platforms (e.g., AWS Redshift, Snowflake, BigQuery).
- Strong knowledge of data modeling, data warehousing, and building ETL/ELT pipelines.
- Experience with version control systems like Github and deployment & CI tools.
- Familiarity with modern data orchestration tools (e.g., Airflow, dbt).
- Excellent communication and collaboration skills.
Responsibilities
- Design, build, and maintain scalable ETL/ELT pipelines to support analytics and machine learning workloads
- Manage and scale data pipelines from internal and external data sources
- Develop and manage robust data models and warehouse structures that support self-service analytics and reporting
- Build and own the automation and monitoring frameworks that captures metrics and operational KPIs for data pipeline quality and performance
- Implement and monitor data quality and validation checks to maintain trust in our data assets
- Work with stakeholders across the business to understand data requirements and ensure data availability, accuracy, and usability
- Optimize data storage and query performance across cloud-based and relational systems.
- Stay current with emerging data engineering tools and architectures
- Responsible for implementing best practices around systems integration, data modeling, optimization, security, performance, and data management to ensure reliability, scalability, and maintainability of the infrastructure
- Empower the business by creating value through the increased adoption of data, data science, and business intelligence landscape
- Collaborate closely with data science and analytics teams to ensure data infrastructure supports model training and deployment
- Research in state-of-the-art methodologies
- Create documentation for learning and knowledge transfer
- Create and audit reusable packages or libraries
- Implement data security and compliance measures
Preferred Qualifications
- 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.
- Experienced in coaching and mentoring team members to foster growth and development.