Designs and maintains ETL pipelines, data models, dashboards, and analytics processes for large datasets. Develops statistical models, complex metrics, and data products; analyzes trends and relationships; and communicates insights through visualizations. Builds, deploys, monitors, and maintains machine learning and GenAI pipelines. Ensures data governance, quality, privacy, security, documentation, and performance across data architecture and management systems.
Key Responsibilities:
- Design, implement, and maintain scalable data pipelines for the extraction, transformation, and loading (ETL) of large datasets
- Create algorithms and statistical models to extract actionable insights.
- Design, develop and implement data models for base tables and dashboards
- Conduct thorough analysis to understand data models, upstream source systems, and trends within datasets
- Query massive data sets to interpret complex relations
- Build complex logics for attributes, metrics and feature banks under engineered datasets and reports
- Leverage GenAI capabilities and machine learning techniques to enhance data analysis capabilities.
- Enable and/or develop AI use cases (ML or GenAI based)
- Test, vet, deploy, maintain and monitor AI pipelines
- Create visualizations and dashboards to communicate data-driven insights.
- Package and serve data products over the various reporting outlets in use
- Adhere to information security and personal data privacy mandates and guidelines in data collection, analysis, access management and reporting
- Implement best practices for data governance, quality and documentation
- Implement best practices and continuously improve data analytics processes
- Develop and maintain data architecture and data management, ensuring data integrity, security, and optimal performance
Education:
Bachelor's degree in IT, Computer Engineering, Computer Science, Data Science
Level of Experience:
Limited Experience (2-5Yrs) in a related field
Technical Skills & Knowledge:
Essential:
- Excellent knowledge of data warehousing and data modelling principle
- Excellent knowledge SQL
- Very good knowledge of Linux and OS Administration
- Excellent knowledge of Python
- Working knowledge of orchestration platforms (e.g. Airflow)
Desirable:
- Good knowledge of telecom core systems and data sets
- Good knowledge of key information security and networking principles
- Good knowledge of Scala
- Good knowledge of spark framework
Certifications & Licensure
Essential:
- SQL (any variant) Certification
- Python Certification
- Hadoop or Data Lakehouse Certification
Desirable:
- GenAI and LLM Engineering Certification
- Spark Certification
- Airflow Certification
Tools & Systems:
Essential:
- Data Warehousing or modern data platform
- Apache Hadoop eco-system
- Power BI (or other visualization tools)
- Python (base, pandas, scikit-learn)
- Agentic Development
Desirable:
- GenAI e2e solutions development
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Home to tech titans like Microsoft and Amazon, Seattle punches far above its weight in innovation. But its surrounding mountains, sprinkled with world-famous hiking trails and climbing routes, make the city a destination for outdoorsy types as well. Established as a logging town before shifting to shipbuilding and logistics, the Emerald City is now known for its contributions to aerospace, software, biotech and cloud computing. And its status as a thriving tech ecosystem is attracting out-of-town companies looking to establish new tech and engineering hubs.
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- Research Centers and Universities: University of Washington, Seattle University, Seattle Pacific University, Allen Institute for Brain Science, Bill & Melinda Gates Foundation, Seattle Children’s Research Institute
