Data & AI Enablement Architect
Role details
Job location
Tech stack
Job description
We're looking for a mission driven data architect who treats data as a product that enables strategic decisions. The selected candidate will partner directly with Newsroom, Product, Marketing, and Advertising stakeholders to model digital and operational data, define business logic, and make it easily accessible through rapidly evolving tools like AI-enabled notebooks and conversational analytics.
This is a highly collaborative, stakeholder facing role, focused on translating business needs into scalable data models that support broader analysis. This is not a backend data engineering role focused only on pipelines and infrastructure. This role will work as part of the Data and Insights team, and provide technical direction for a small team of data engineers and analysts.
What You'll Do
- Partner directly with stakeholders - act as a primary interface between business teams and the data platform, participating in sessions with product managers, marketers, and editors to define data needs.
- Translate stakeholder analysis needs/queries into clean, reusable dbt models.
- Operationalize and steward core business metrics through models and the semantic layer that powers reporting, experimentation, and downstream tools.
- Help stakeholders effectively adopt and use data tools, and enable self-service AI-enabled conversational analytics through tools like Hex.
- Set standards for modeling, documentation, reuse across the D&I team.
- Evolve the data platform, and in particular, strengthen data quality monitoring for prioritized models and data sets.
- Provide technical direction and mentoring for data engineers and analysts.
- Ensure reliable data pipelines (both via Fivetran and custom interfaces).
- Contribute to an inclusive and positive work environment
Requirements
Do you have experience in Terraform?, * 5+ years hands on experience in analytics data engineering
- Intellectually curious and intrinsically motivated
- Comfortable leading ambiguous conversations without predefined requirements
- Proven experience working directly with non-technical stakeholders
- Experience enabling data access through tools like Hex, notebooks, or semantic layers
- Familiarity with AI-assisted analytics/conversational querying; interest in building self-service data experiences
- Professional experience with dbt, Airflow, Python, and Terraform
- Advanced SQL skills including use of analytic window functions and query optimization
- Experience with web and/or application monitoring tools (e.g. GA4, Blaze, Adobe, Amplitude, etc.)
- Experience with Google Cloud Platform and/or AWS
- Working knowledge of core statistical concepts and their applications