Sr. Lead Data Engineer

In this role, you design and maintain reliable data pipelines and analytics capabilities to support engineering, risk, and business decisions. You will lead a high-performing team within the Behavioral Insights Team, shaping data products and governance to improve reliability, risk visibility, and delivery efficiency. You’ll define SLIs/SLOs, automate dashboards and near-real-time views, and ensure strong data management and controls. A key hook is applying AI-assisted methods to accelerate data platform work while maintaining security and data sensitivity.

• Track change and release health and influence safer release practices
• Produce capacity forecasts and validate scaling policies with engineering
• Monitor FinOps metrics and cost observability, highlight reliability–cost tradeoffs
• Ensure risk and controls compliance, guardrail adherence, and audit readiness
• Leverage enterprise-authorized AI capabilities for data engineering workflows and documentation
• Monitor data risk and controls for data access and use
• Define data contracts for telemetry sources and document end-to-end data lineage
• Deliver automated pipelines for scheduled reporting and near-real-time dashboards with RBAC self-service
• Lead weekly reliability reviews and monthly leadership reviews, maintain action logs
• Oversee workflow and mentorship for junior data engineers and visualization researchers
• Apply reuse-first, AI-assisted practices ensuring traceability and alignment to resiliency and security expectations

• Formal training or certification on data engineering concepts and advanced applied experience in data analytics/BI/operations analytics
• Strong SQL skills (CTEs, window functions, performance-aware querying)
• Experience using enterprise-authorized AI capabilities with validation habits and data sensitivity awareness
• Ability to review and validate AI-assisted outputs and escalate when uncertain
• Hands-on experience building dashboards in Tableau/Power BI/Looker
• Experience with ITSM tools (e.g., ServiceNow) and understanding incident/change/problem concepts
• Strong data storytelling skills to translate operational findings into practical improvements

• leadership and mentoring
• data storytelling
• communication of complex insights to stakeholders
• SQL (CTEs, window functions)
• Tableau
• Power BI