Drive the design and governance of enterprise-level data pipelines for scalable ingestion, transformation, and storage across multiple ecosystems

Provide strategic oversight and practical expertise in handling complex datasets, ensuring the preparation of highly optimized, scalable systems for analytical applications

Guide and own the use of Python and advanced data processing frameworks (e.g., Pandas, NumPy) for mission-critical exploratory data analysis (EDA)

Oversee the adoption and operational alignment of advanced cloud tools and infrastructure, standardizing scalability and ensuring ongoing performance excellence

Demonstrate expertise in Databricks, including full lifecycle oversight of Lakehouse architecture, workflows, and ETL optimization

7+ years in data engineering roles, demonstrating a high level of self-direction and strategic ownership

Minimum of 2 years in leadership or managerial roles driving teams and data strategy at scale

Participation in at least two full-cycle projects or multiple projects spanning various development phases

Advanced proficiency in Python for complex data workflows and modeling using tools like Pandas, NumPy, and domain-specific libraries

Extensive experience handling and strategizing for large-scale structured/unstructured datasets, particularly device, medical, IoT, or hardware-generated logs

Comprehensive knowledge and practical experience with Databricks workflows, Lakehouse architecture, and ETL process optimization

In-depth expertise working closely with device-derived data such as IoT logs, hardware diagnostics, or medical device outputs

Exceptional communication and leadership capabilities coupled with fluency in English at a C1 level or higher

Industry-recognized experience with PyTorch, TensorFlow, or equivalent advanced AI/ML processing systems

Ability to define and integrate AI/ML strategies while effectively utilizing pre-built models for real-world applications