Develop and maintain reliable data pipelines for ingesting, transforming, and storing data from multiple sources

Manipulate complex datasets and preprocess large-scale logs and time-series data using state-of-the-art tools and frameworks

Leverage your knowledge of Python (Pandas, NumPy, etc.) to perform exploratory data analysis (EDA) and prepare data for business and analytical use cases

Work collaboratively with cloud-based tools and infrastructure, focusing on scalability and performance optimization

Utilize tools like Databricks, if experienced, or demonstrate a willingness to quickly ramp up on Databricks’ Lakehouse, Workflow, and ETL functionalities

Proven experience of over 5 years as a Data Engineer or in a similar role, demonstrating a self-driven and problem-solving mindset

At least 1 year of relevant leadership experience

Expertise in Python for data processing, analysis, and manipulation (e.g., Pandas, NumPy, and advanced libraries)

Proven experience working with large-scale logs, time-series data, and structured/unstructured data in diverse formats, preferably hardware/device data logs

Knowledge of Databricks, including workflows, ETL processes, and working with Lakehouse architecture, is highly desirable

Experience working with device data manipulation, such as medical data, IoT device data, hardware data, or similar

Excellent command of written and spoken English (B2+ level)

Exposure to PyTorch or TensorFlow for advanced data processing and AI/ML tasks

Understanding of or experience with AI/ML models, along with the ability to apply predefined machine learning models to datasets