Principal Data Engineer (AWS, Databricks, Ai, ML Flow, Data Architecture, Apache Airflow)

Lead architecture, design, and engineering of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI workloads.

Design and build secure, reliable data ingestion, transformation, enrichment, and consumption pipelines including governed lakehouse and modern data platform architectures using cloud-native technologies.

Provide hands-on technical leadership, design reusable data products and self-service capabilities, and collaborate with cross-functional teams to mature R&D initiatives into enterprise-grade production solutions.

12-18 years of relevant experience owning complex enterprise data engineering solutions.

Strong programming skills in Python, PySpark, and advanced SQL expertise including data modeling and query optimization.

Experience building and operating large-scale cloud-native data platforms (AWS, Azure, or equivalent) using distributed data processing technologies like Apache Spark and workflow orchestration tools such as Apache Airflow.

Bachelor's degree in Computer Science, Engineering, Information Systems, or related technical discipline (or equivalent practical experience).

Demonstrated ability to deliver production-quality, scalable, secure, and governed data platforms while balancing innovation, maintainability, and cost in an evolving R&D environment.

Experienced in leading cross-functional teams and influencing technical decisions without formal management authority, focusing on providing technical direction and mentorship.

Skilled at translating ambiguous research and innovation requirements into practical architectures and reusable engineering capabilities that support AI and machine learning lifecycle.