At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Pricing is at the core of
Principal Databricks Data Engineer Experience Required: 12–18 Years Key Requirements • 12–18 years of overall Data Engineering experience • 8+ years of experience with enterprise Data Warehouse and Data Lake platforms • 5+ years of hands-on
Principal Data Engineer – Databricks | Spark | Delta Lake | PySpark | Data Lakehouse | AWS/Azure Job Description Location: Toronto Work Model: Onsite (4 days/week) Key Requirements • 12–18 years of overall Data Engineering experience.
Principal Databricks Data Engineer Experience Required: 12–18 Years Key Requirements 12–18 years of overall Data Engineering experience 8+ years of experience with enterprise Data Warehouse and Data Lake platforms 5+ years of hands‑on experience with Databricks
Responsibilities Design and build realtime and batch data pipelines for fraud detection and AML monitoring. Process streaming transaction data using Apache Spark and Kafka. Develop ETLELT workflows supporting customer transaction and risk datasets. Ensure data quality traceability
Job Title: Java Big Data Engineer Location: Toronto, ON (Hybrid – 4 days onsite per week) Employment Type: Contract About the Role We are looking for an experienced Java Big Data Engineer to join a dynamic
At the company, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Pricing is at the core
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Pricing is at the core of
Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and
Job Description Design, develop, and support scalable batch and real-time data processing solutions using Apache Spark (Core, SQL, Streaming), Scala, PySpark, Hadoop, and Hive. Build and maintain enterprise-grade ETL/ELT pipelines across cloud platforms including AWS, Azure, and