Act as a subject matter expert for integrated data products and foundational data systems

Enforce data engineering guardrails, standardized workflows, and engineering standards to ensure scalable and reliable data platforms

Optimize data pipeline costs and recovery processes using telemetry, automation, and financial efficiency practices

Manage feature stores, machine learning pipelines, and data warehouses, ensuring robust modeling, governance, and adherence to SLAs

Implement privacy-by-design principles, data contracts, and production-grade ML pipelines for secure and efficient operations

Maintain technical oversight to ensure alignment with AI enablement strategies and enterprise goals

Promote reuse of components, patterns, and best practices across data engineering teams

Mentor and elevate data engineers, contribute to setting and refining standards, and build communities of practice

Solve complex, cross-team data challenges and drive systemic improvements with minimal supervision

Influence strategic roadmaps, balance trade-offs, and align technical decisions with business goals

Collaborate effectively with cross-functional teams and external partners to deliver impactful solutions

Lead through ambiguity, drive organizational change, and support the development of new capabilities

At least 3 years of proven experience in Data Engineering

Expertise in designing secure ELT and ML data pipelines, optimizing job cost and latency, and managing context artifacts for reliability

Proficiency in enabling safe agent actions, federated data sharing, and robust data warehousing using AWS

Experience tuning Snowflake for performance, secure data sharing, and governance

Strong ability to design and manage scalable EMR/Apache Spark clusters for distributed data processing

Proven experience leading EMR/Apache Spark-based ETL and analytics solutions

Skilled in applying cost profiling and fault-tolerant AI service patterns

Proficient in implementing verification, monitoring, backup, and recovery procedures for data systems

Mastery of data product lifecycle management, ML orchestration, and enterprise modeling

Fluent English communication skills, both written and spoken, at a B2+ level or higher

Experience with Kubernetes or containerized environments for data workflows

Knowledge of real-time data streaming technologies such as Apache Kafka or AWS Kinesis