• Lead, hire, and develop a team of data engineers, including performance management and career growth • Own end-to-end delivery, including sprint planning, prioritization, and execution • Establish engineering standards and ensure high-quality, consistent output • Partner with cross-functional stakeholders to align data initiatives with business priorities • Design and evolve scalable data architecture across ingestion, transformation, storage, and consumption layers • Build and maintain high-volume ETL/ELT pipelines for real-time and batch data • Lead data modeling, governance, and data quality practices • Drive adoption of AI and automation across data engineering workflows • Partner with data science teams to operationalize models and embed AI into data products • Evaluate and implement emerging tools with a focus on practical impact

• This role focuses on building the data foundations that enable advanced analytics and machine learning at scale. • You will design and develop data pipelines and architectures that support real-time insights and AI-driven applications across the business. • Working with large, complex datasets, you will ensure data is reliable, accessible, and optimized for both analytics and machine learning use cases. • Develop data models and warehouse schemas optimized for analytics and machine learning. • Build and maintain data ingestion frameworks using streaming systems (Kafka, Kinesis) and batch processing (Spark, Airflow). • Build and manage feature stores to support ML model training and inference. • Integrate data from multiple sources into a unified architecture. • Implement data quality validation frameworks and monitoring systems. • Optimize data pipelines for performance, scalability, and cost efficiency.

• Lead the evolution of intelligent platforms across our enterprise customer division. • Champion strategic initiatives leveraging data, analytics, and artificial intelligence to enhance customer experiences. • Shape data architecture, artificial intelligence strategy, and governance models for automation, MLOps, and insights. • Collaborate with business and technology partners for modern, secure, and adaptive data ecosystems and AI solutions. • Contribute to establishing a future-ready data foundation.

• Build and support non-interactive (batch, distributed) & real-time data pipelines • Build fault-tolerant, self-healing, adaptive, and highly accurate data computational pipelines • Provide consultation and lead implementation of complex programs • Develop and maintain documentation for assigned systems and projects • Tune queries over billions of rows of data in a distributed query engine • Perform root cause analysis for software/business process issues • Implement and maintain dbt transformation models, CI pipelines, data contracts • Build and monitor data quality gates and freshness SLOs • Optimize BigQuery cost and performance with query tuning, storage design • Implement platform hardening controls including retries, dead-letter queues