Senior Data Engineer

In this role you design and expand enterprise data infrastructure to empower internal teams to access data at scale. You’ll work within the AI, Data and Innovation organization, shaping data pipelines and platforms for AI-enabled products. You will own production systems, respond to incidents, and drive data quality and observability. This is a fast-paced, data-centric environment where you’ll lead architecting scalable solutions and mentor others, with a clear impact on product and business outcomes.

• Design, develop, and maintain high-performance data pipelines using Airflow, DBT, and Python
• Architect and optimize Snowflake-based data warehouse and lakehouse as the single source of truth
• Lead integration of diverse structured and unstructured data sources (web data, third-party APIs)
• Define roadmap priorities to anticipate internal needs and drive AI capabilities
• Act as trusted advisor to leadership on data infrastructure investments and AI-readiness
• Collaborate with ML engineers, data scientists, and product managers to translate business needs into scalable data solutions
• Define, monitor, and enforce data quality SLAs across pipelines and products
• Participate in on-call rotation, perform root-cause analysis, and drive remediation and postmortems
• Triage production issues, escalate appropriately, and coordinate with stakeholders and leadership
• Mentor junior engineers on code quality, data architecture, and operational excellence
• Participate in architectural decisions and long-term strategy for enterprise data infrastructure
• Contribute to runbooks and on-call documentation to reduce time-to-resolution

• Expert-level SQL for large datasets
• Strong Python for distributed computing and robust APIs
• Production-level experience with large-scale batch and streaming data processing
• Hands-on DBT experience for advanced data modeling
• Deep Snowflake data warehouse design and optimization knowledge
• Experience owning production systems and on-call processes
• Solid understanding of data architectures (data lakes, event-driven, ETL/ELT, data mesh)
• Cloud platform experience (GCP and/or AWS) and infrastructure as code (Terraform)
• Monitoring/observability tooling experience (Datadog, Monte Carlo, Grafana)
• CI/CD practices for data workflows (testing, version control)

• Excellent communication under pressure
• Strategic and product-oriented thinking
• Leadership and mentorship
• SQL
• Python
• Airflow