Data Science Analyst
Job Description
We are hiring a Data Science Analyst to support enterprise-scale data engineering and analytics initiatives for Dominion. The ideal candidate will have strong expertise in SQL, ETL/ELT development, data warehousing, and production-grade pipeline support across cloud and on-prem environments. This role requires hands-on experience with enterprise data platforms, DataOps practices, observability, and scalable pipeline architecture supporting analytics and AI/ML initiatives. The candidate should be comfortable working in high-volume production environments, optimizing performance, ensuring data quality, implementing auditability and lineage, and supporting mission-critical data operations including occasional weekend or off-business-hours support.
Requirements
- Advanced expertise in SQL and ELT/ETL development
- 5–7+ years of Data Engineering experience
- Strong experience with Oracle Exadata, Snowflake, or similar platforms
- Hands-on experience with Talend, dbt, Informatica, or similar ETL tools
- Strong understanding of dimensional modeling and data warehousing architecture
- Experience building and supporting large-scale production data pipelines
- Strong scripting skills using Python and shell scripting
- Experience with data virtualization tools such as Denodo, Dremio, Composite, or Starburst
- Experience with DataOps, CI/CD pipelines, and observability tools
- Strong troubleshooting and performance tuning skills
- Ability to support ETL process operations during off-business hours if required
- US Citizen or Green Card holder only
Responsibilities
- Design and maintain scalable enterprise data pipelines
- Develop and optimize ETL/ELT workflows for analytics platforms
- Ensure reliability, restartability, and auditability of production pipelines
- Implement data quality monitoring and lineage tracking processes
- Support hybrid cloud and on-prem data warehouse environments
- Optimize SQL queries and warehouse performance
- Collaborate with analytics, engineering, and architecture teams
- Implement DataOps best practices and CI/CD automation
- Support data virtualization and ingestion strategies
- Assist with enterprise reporting, AI/ML, and advanced analytics initiatives
- Provide production support and incident resolution for data systems
Technologies & Skills
Benefits
- Hybrid work environment
- Competitive W2 compensation
- Opportunity to work on enterprise-scale data platforms
- Exposure to cloud and advanced analytics technologies
- Collaborative engineering environment
- Long-term project opportunity
- Hands-on experience with modern DataOps practices
- Opportunity to support AI/ML data initiatives