• Data Pipeline Development: Design and maintain ETL (Extract, Transform, Load) processes to move data from diverse sources into data warehouses/data lakes. • Infrastructure and Databases: Design and implement scalable databases (relational and NoSQL), ensuring security, performance, and data integrity. • Big Data and Distributed Processing: Manage large volumes of data using distributed processing tools, essential for organizations with high data volumes. • Cloud Computing and Automation: Implement cloud solutions (AWS, Azure, GCP) and automate workflows (IaC/DevOps). • Collaboration and Structuring: Work with analytics teams to understand business needs, ensuring data is prepared for dashboards and AI models.

• Define architecture and deploy a Delta Lake platform for real estate market data at aMORA • Implement systems for scalable consumption of various existing data sources: CSV files, APIs, and partner feeds • Expose data in layered models (e.g., DMBOK)

• As a member of our Data Engineering team, you will be working with fast-paced innovative companies, leveraging Cloud as the key driver of their transformation. • They will look at you as their trusted advisor, someone they can rely on and who will be there to help them along their journey on Google Cloud. • You will be expected to cover a large spectrum of technology topics like ETL/ELT, Data Warehousing, Distributed Data Processing, and Streaming Data systems. • NOTE: This role is a 6 month Contract w/ potential of extension

• Design, build, and optimize large-scale distributed data pipelines using Apache Beam / Google Cloud Dataflow, Apache Spark, or Apache Flink. • Develop ETL/ELT workflows on GCP integrating multiple structured and unstructured data sources. • Address schema, type conversion, and performance optimization challenges when migrating data warehouses to BigQuery. • Implement observability, monitoring, logging, and error-handling within pipelines. • Collaborate with Data Analysts to integrate validation rules, QA checks, and automated testing into the pipeline lifecycle. • Support large-scale data migrations and performance tuning for high-volume workloads.