Data Engineer – Data Platform
il y a 2 jours
Company Overview
Yakeey is a fast‑growing PropTech company headquartered in Casablanca, Morocco. Our mission is to simplify and accelerate real‑estate transactions by connecting buyers, sellers, renters and agents through a single digital platform. We operate a multi‑product portfolio—including a property marketplace, a credit application and processing platform and a valuation tool—powered by a modern data architecture that underpins analytics, reporting and AI‑driven services.
Role Summary
As a Data Engineer focused on platform stability and analytics, your mission will be to maintain and enhance a reliable, high‑performance data stack that empowers product and operations teams with actionable insights. You will leverage AWS technologies (Postgres RDS, Redshift, S3), build and manage ELT pipelines with Airbyte, model data with dbt and orchestrate workflows with Prefect. Your work will ensure that key performance indicators and data products are accurate, timely and tailored to support decision‑making, improve processes and inform the development of our one‑stop‑shop services.
Key Responsibilities
Maintain and optimize our data platform (RDS, Redshift, S3), ensuring high availability, performance and cost efficiency to support analytics and operations.
Develop and manage ELT pipelines with Airbyte to ingest data from diverse sources and keep the data warehouse and data lake up to date.
Model and transform data using dbt to deliver clean, well‑structured datasets and meaningful KPIs for product and operations teams.
Orchestrate data workflows with Prefect, automating tasks such as data ingestion, transformation and quality checks.
Collaborate with product, operations and analytics teams to understand data needs, define metrics and build data products that support decision‑making.
Implement and enforce data governance practices, including monitoring, testing and documentation, to ensure data accuracy and trustworthiness.
Proactively monitor pipeline performance and resolve issues, continuously improving reliability and scalability.
Stay current with emerging tools and best practices in data engineering to enhance the stack and support advanced use cases like MCP‑based agents and RAG architectures.
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