• Own Databricks platform strategy: architecture decisions, Unity Catalog migration, cost optimization, and operational excellence. • Drive data pipeline development: real-time and batch pipelines using PySpark, Spark Structured Streaming, and Delta Lake. • Establish data quality standards: validation frameworks, monitoring, alerting, and SLA management. • Partner with Data Architects to translate Enterprise Data Models into performant physical implementations. • Integrate streaming infrastructure: Kafka-based event-driven ingestion and processing. • Build reporting capabilities: dashboards, KPIs, and self-service analytics for internal and client-facing use. • Drive data storytelling: executive presentations, client reporting, and strategic business insights. • Develop metrics frameworks: campaign performance, audience quality, and platform health KPIs. • Support ad-hoc analysis for campaign optimization and business questions. • Lead and grow DE and Analytics teams: hiring, mentoring, performance management, and career development. • Coordinate with offshore partners to extend team capacity and manage distributed development workflows. • Partner cross-functionally with Data Science, Product, and Client Success to align data capabilities with business needs. • Champion engineering best practices: code reviews, CI/CD, testing, documentation, and observability.

• Drive ML/AI strategy: audience propensity models, lookalike modeling, and segmentation algorithms that improve campaign performance. • Own ROAS optimization: develop and refine models that maximize return on ad spend for clients. • Build production ML systems: feature engineering, model training, batch/online inference, and serving infrastructure. • Establish MLOps practices: model versioning, monitoring, drift detection, and reproducible training pipelines. • Champion experimentation: A/B testing frameworks, statistical rigor, and data-driven decision making. • Lead and grow the Data Science team: hiring, mentoring, performance management, and career development. • Partner with Product and Client Success to translate business requirements into ML solutions and communicate model capabilities. • Coordinate with Data Platform team to ensure reliable data foundations and feature pipelines for modeling. • Translate complex ML concepts into actionable insights for business stakeholders and executives. • Set technical direction and foster a culture of innovation, rigor, and continuous improvement.

• Lead, mentor, and grow a high-performing team of software developers. • Provide career guidance, support personal development, and drive performance through clear feedback and coaching. • Champion a culture of ownership, learning, and inclusivity within the team. • Recruit, onboard, and retain top talent, ensuring a healthy and collaborative engineering culture. • Guide architecture and technical design of complex, distributed software systems across both product and platform layers. • Participate in design and code reviews to uphold standards and best practices. • Balance immediate deliverables with long-term architectural vision, scalability, and maintainability. Promote high code quality and maintain a pragmatic approach to technical debt. • Own the planning, resourcing, and execution of software development projects using Agile/Scrum methodologies. • Align team objectives with business priorities in collaboration with Product and Design. • Track delivery progress, address blockers, and proactively manage risks and dependencies. Drive accountability and continuous delivery of value to customers. • Implement and evolve engineering processes and workflows to maximize team effectiveness. • Build a culture of iteration, feedback, testing, and continuous improvement. • Ensure robust test practices, monitoring, and deployment strategies are in place. • Lead post-mortem reviews and implement learnings to drive operational excellence. • Communicate project status, technical challenges, and decisions across all levels of the organization. • Translate complex technical concepts for non-technical stakeholders. • Represent engineering in strategic planning, roadmap discussions, and customer interactions when needed. Collaborate across teams to build shared understanding and alignment. • Encourage innovation and experimentation within teams. • Stay current on industry trends and emerging technologies.

• Own the shipping rhythm of your squad, ensuring frequent, meaningful customer-facing releases while balancing speed and engineering quality. • Drive tight collaboration between Product and Engineering, translating customer problems into well-sliced deliverables and reducing time to value. • Raise the bar on performance by setting clear expectations, coaching engineers, proactively addressing underperformance, and ensuring the right team is in place. • Embed a culture of urgency and customer impact, framing discussions around releases, outcomes and time to ship rather than task completion. • Leverage AI and modern tooling to increase velocity, improve decision-making and continuously strengthen the team’s engineering craft.