Senior Data Science Analyst
Listed on 2026-10-18
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IT/Tech
Data Engineering, Data Warehousing, Data Science Manager, Data Analyst
Senior Data Science Analyst IT | Software Design/Developer Richmond , Virginia Contract Sep 22, 2026
Position: Senior Data Science Analyst
Location: Richmond, Virginia
Duration: Contract
Job : 179944
Job OverviewThe Senior Data Science Analyst will play a critical role in designing, building, and optimizing scalable, reliable data pipelines at an enterprise level. This position requires hands-on expertise in data engineering, technical leadership, and a deep understanding of data warehousing, ETL/ELT processes, and data governance. The role involves working closely with architecture and platform teams to enhance the overall data ecosystem while ensuring high standards for quality, security, and performance.
Responsibilities- Design and build end-to-end data pipelines and ETL/ELT solutions to support analytics, reporting, and AI/ML use cases.
- Develop, test, and deploy scalable patterns for batch and incremental data processing.
- Optimize data modeling, transformation logic, and performance strategies.
- Evaluate and integrate tools, frameworks, and platform capabilities to meet project requirements.
- Ensure data pipelines meet security, access control, and governance requirements.
- Collaborate with governance and Data Ops teams on metadata, lineage, and auditability.
- Lead performance tuning efforts across pipelines, warehouses, and workloads.
- Support production troubleshooting and root cause analysis for data quality incidents.
- Participate in design and code reviews to enforce quality and best practices.
- Partner with infrastructure, application, and architecture teams to design complex data transformations.
- Translate ambiguous requirements into well-designed technical solutions.
Position: Senior Data Science Analyst
Location: Richmond, Virginia
Duration: Contract
Job : 179944
Job OverviewThe Senior Data Science Analyst will play a critical role in designing, building, and optimizing scalable, reliable data pipelines at an enterprise level. This position requires hands-on expertise in data engineering, technical leadership, and a deep understanding of data warehousing, ETL/ELT processes, and data governance. The role involves working closely with architecture and platform teams to enhance the overall data ecosystem while ensuring high standards for quality, security, and performance.
Responsibilities- Design and build end-to-end data pipelines and ETL/ELT solutions to support analytics, reporting, and AI/ML use cases.
- Develop, test, and deploy scalable patterns for batch and incremental data processing.
- Optimize data modeling, transformation logic, and performance strategies.
- Evaluate and integrate tools, frameworks, and platform capabilities to meet project requirements.
- Implement enterprise-grade error handling, restartability, and monitoring for data pipelines.
- Ensure data pipelines meet security, access control, and governance requirements.
- Collaborate with governance and Data Ops teams on metadata, lineage, and auditability.
- Lead performance tuning efforts across pipelines, warehouses, and workloads.
- Support production troubleshooting and root cause analysis for data quality incidents.
- Participate in design and code reviews to enforce quality and best practices.
- Partner with infrastructure, application, and architecture teams to design complex data transformations.
- Translate ambiguous requirements into well-designed technical solutions.
- 5 to 7+ years of Data Engineering experience.
- Bachelor's degree or higher in Computer Science, Information Systems, Mathematics, or a related field.
- Advanced expertise in SQL, ELT patterns, and performance tuning.
- Strong experience with Oracle Exadata, Snowflake, or similar cloud/on-prem data warehouses.
- Hands-on experience with enterprise ETL/ELT platforms (e.g., Talend, dbt, Informatica).
- Deep understanding of data warehousing architecture and dimensional modeling.
- Proficiency in scripting languages such as Python and shell scripting.
- Experience with data virtualization tools (e.g., Denodo, Composite, Dremio, Starburst).
- Knowledge of Data Ops practices, CI/CD, and observability.
- Ability to work independently and demonstrate strong problem-solving skills.
- Experience supporting AI/ML or advanced analytics pipelines.
- Cloud platform experience (AWS, Azure, or GCP).
- Experience influencing enterprise data standards or reference architecture.
- Expertise in optimizing cost and performance in cloud data warehouses.
- Hands‑on experience with Cribl, Apache Kafka, Kafka Connect, Spark Streaming, or Apache…
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