About the position
The position involves a comprehensive role in Data Architecture, Data Modeling and Warehousing, Data Access, Data Pipelines, and MLOps/DevOps. The candidate will be responsible for designing and implementing data platforms and infrastructure, ensuring considerations around data storage strategy, security, latency, reliability, scalability, and costs. The role requires providing thought leadership on data management and governance from a data architecture perspective, including data granularity and cross-application data access design. Additionally, the candidate will create relational and NoSQL data models, design and build databases, and implement data lakes for analytics and AI/ML use cases. The position also involves designing database query infrastructure for custom machine learning applications and implementing ETL pipelines to ingest data from various sources. Furthermore, the candidate will manage production ML workflows and ensure continuous monitoring of production models' effectiveness.
Responsibilities
- Design and implement data platforms and infrastructure considering data storage strategy, security, latency, reliability, scalability, and costs.
- Provide thought leadership on data management and governance from a data architecture perspective.
- Design and build an enterprise data architecture ensuring new applications conform to existing data models.
- Create relational and NoSQL data models for diverse data consumers.
- Design and build databases hosting enterprise data with robust design principles.
- Design and implement data lakes for analytics and AI/ML use cases.
- Design and build database query infrastructure for custom machine learning applications.
- Design APIs to support data requirements for business intelligence dashboards.
- Design and implement ETL pipelines to ingest data from transactional systems and other sources.
- Design and implement automated schedules for monitoring data pipelines, data quality, and data lineage.
- Design and implement the machine learning lifecycle at scale, including data infrastructure for training/testing ML models.
- Manage production ML workflows ensuring automated CI/CD capabilities are built into the workflow.
- Design and implement alerts/dashboards for continuous monitoring of production models' effectiveness.
Requirements
- Ability to work closely with data analysts, data scientists, business analysts, and business stakeholders.
- Comfortable working in a cross-functional team and collaborating with peers during the project lifecycle.
- Open to travel based on project and team locations.
Nice-to-haves
- Strong preference for candidates with cloud certifications (Azure).
- Start-up experience is a plus.
