Data Engineer: Real-Time Pipelines & Cloud Scale (Hybrid)

**The Story So Far: We’re Building a Global Brand in Real Estate

**Huspy is one of the leading property technology companies in EMEA.

Launched in 2020, we now operate in multiple cities across the UAE and Spain, expanding into Saudi Arabia and 3 more European markets by 2026. Today, we own the largest portion of the UAE mortgage market and are one of the fastest-growing players in every European city we’ve entered.

We’ve raised over $140 million (Series A and Series B) from the world’s top investors, including Sequoia Capital and Balderton Capital, to reshape the homebuying journey through powerful technology and agent-first tools.

We’ve built a SuperApp that empowers real estate agents and mortgage brokers, bringing cutting‑edge technology to one of the world’s most traditional industries. We’re transforming how property transactions happen — faster, smarter, and better for everyone.

We’re not slowing down. The question is: will you be part of what’s next?

**The Main Event: What You’ll Drive, Build, and Own

• Design and Scale Data Pipelines: Build and maintain reliable batch and streaming pipelines that connect our applications, events, and external sources to Snowflake, ensuring data flows seamlessly across our platform.
• Streaming Data at Scale: Work with Kafka, Schema Registry, and event-driven architectures to power real-time data products and ensure downstream systems get trustworthy, well-structured events.
• Pipeline Orchestration: Use Airflow to orchestrate complex workflows, automate dependencies, and ensure data availability for analytics and business processes.
• Data Infrastructure & Cloud Services: Leverage AWS tools (ECR, ECS, S3, etc.) to build scalable, containerized data services and optimize storage, compute, and costs.
• Data Quality & Observability: Implement testing frameworks, monitoring, and alerting for data pipelines to ensure reliability and trust at scale.
• Infrastructure as Code: Manage data infrastructure with tools like Terraform, Helmfile, or CloudFormation, automating deployments and ensuring reproducibility.
• Optimize for Scale and Performance: Continuously improve the throughput, latency, and cost efficiency of pipelines, ensuring the platform scales with the business.
• Modeling & Governance: Apply strong data modeling fundamentals (e.g., Kimball, Data Vault) and enforce governance around PII, compliance, and lifecycle management.
• Collaboration for Impact: Partner with analysts, business stakeholders, and product teams to translate requirements into well-designed pipelines and models that unlock business value.
• Automation & CI/CD: Implement testing, linting, and automated deployments for data pipelines to ensure high-quality, production-ready code.