Professional Data Engineer

Property Finder·LinkedIn·Cairo, Egypt·On-site·12 hours ago

About the role

Property Finder is the leading property portal in the Middle East and North Africa (MENA) region, dedicated to shaping an inclusive future for real estate while spearheading the region's growing tech ecosystem. At its core is a clear and powerful purpose: To change living for good in the region.

Founded on the value of great ambitions, Property Finder connects millions of property seekers with thousands of real estate professionals every day. The platform offers a seamless and enriching experience, empowering both buyers and renters to make informed decisions. Since its inception in 2007, Property Finder has evolved into a trusted partner for developers, brokers, and home seekers. As a lighthouse tech company, it continues to create an environment where people can thrive and contribute meaningfully to the transformation of real estate in MENA.

Position Summary:We are seeking a Professional Data Engineer to join our dynamic team, where you will play a crucial role in developing and maintaining robust data solutions.

As a Professional Data Engineer, you will collaborate with data science, business analytics, and product development teams to deploy cutting-edge techniques and utilise best-in-class third-party products.
The Data team operates with engineering precision, prioritising security, privacy, and regulatory compliance in every initiative.
As a Professional Data Engineer, you will contribute to the team's commitment to utilising the latest tools and methodologies, ensuring that our data solutions align with industry best practices.
Our Tech Stack: Languages: SQL & Python Pipeline orchestration tool: Dagster (Legacy: Airflow) Data stores: Redshift, Clickhouse Platforms & Services: Docker, Kubernetes PaaS: AWS (ECS/EKS, DMS, Kinesis, Glue, Athena, S3 and others.) ETL: FiveTran & DBT for transformation IaC: Terraform (with Terragrunt) GenAI: Bedrock, LangChain, LLMs

Key Responsibilities

Develop and maintain ETL pipelines using SQL and/or Python.