Senior Data Engineer at Property Finder

Dubai, Dubai, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

23 Feb, 26

Salary

0.0

Posted On

25 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, SQL, Python, Spark, AWS, Data Pipelines, Dimensional Modeling, Data Warehousing, Orchestration, Generative AI, Machine Learning, Collaboration, Mentoring, Automation, CI/CD, Real Estate

Industry

Software Development

Description

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 looking for a Senior Data Engineer to design, build, and scale the core data infrastructure that powers analytics, AI/ML, and Generative AI solutions across the organization. You will own end-to-end data pipeline development, drive architectural decisions, and ensure our data ecosystem is reliable, scalable, cost-efficient, and optimized for advanced AI use cases. Key Responsibilities: Core Engineering Architect, build, and optimize end-to-end data pipelines (batch + streaming) with a strong focus on performance, scalability, and cost efficiency. Design and maintain high-quality data models to support diverse analytical, ML, and GenAI workloads. Optimize SQL, Python, Spark/PySpark, and transformation workflows for performance at scale. Maintain and evolve data integrations across internal and external systems. Contribute to platform engineering, infrastructure automation, and the reliability of the data environment. Architecture & Strategy Lead architecture discussions, influence technical direction, and propose scalable, future-proof solutions. Build data solutions that are innovative, modern, and avoid legacy approaches. Contribute to the development of self-service data layers enabling stakeholders across Product, Engineering, DS/ML, and Commercial teams. GenAI & Advanced Use Cases Integrate Generative AI capabilities into data engineering workflows (data augmentation, enrichment, vectorization, embeddings, LLM integration, etc.). Build and maintain the data foundations required for AI/ML platforms (feature pipelines, real-time data flows, retrieval infrastructure). Collaborate with ML and GenAI teams to ensure data architecture supports model training, inference, and evaluation at scale. Collaboration & Leadership Work closely with cross-functional teams (Data Science, Product, ML, DevOps, Analytics, Commercial) to deliver data solutions that drive business impact. Uphold high standards of data quality, governance, and reliability. Mentor other engineers and contribute to building a strong engineering culture. Communicate clearly and manage stakeholders to align on priorities and timelines. Impact of the Role As a Senior Data Engineer, you will shape the data backbone that powers strategic decision-making, AI initiatives, and next-generation product features. Your work will directly elevate the organization’s ability to build intelligent, GenAI-powered experiences and deliver scalable, high-impact data solutions. Tech Stack Languages: Python, SQL Streaming: Kafka, Kinesis Data Stores: Redshift, ClickHouse, S3 Orchestration: Dagster AWS: EKS/ECS, DMS, Glue, Athena, Lambda, Bedrock, Quick Suite, DynamoDB ETL/ELT: Fivetran, dbt IaC: Terraform + Terragrunt Desired Qualifications: 7+ years as a Data Engineer, working with large-scale, production-grade data systems. Expert in SQL, Python, Spark/PySpark, and modern data warehousing techniques. Strong AWS experience, including serverless and scalable architectures. Proven experience designing performant, cost-efficient data pipelines (batch + streaming). Deep understanding of dimensional modeling, data warehouses, and industry best practices. Proficient with orchestration tools (Dagster, Airflow, Step Functions). Hands-on experience building automation and CI/CD workflows for data platforms. Practical experience applying Generative AI or LLM-based services to data workflows (e.g., data enrichment, classification, embedding pipelines, retrieval workflows). Preferred Experience: Experience with Real Estate Familiarity with ETL tools: Fivetran, dbt, Airbyte. Experience with real-time analytics solutions (ClickHouse, Pinot, Rockset, Druid). Familiarity with BI tools (QuickSight, Looker, PowerBI, Tableau). Exposure to tagging/tracking tools (Snowplow, Tealium). Experience with Terraform & Terragrunt. Knowledge of GCP or Google Analytics is a plus. Our promise to talent At Property Finder, we believe talent thrives in an environment where you can be your best self. Where you are empowered to create, elevate, grow, and care. Our team is made up of the best and brightest, united by a shared ambition to change living for good in the region. We attract top talent who want to make an impact. We firmly believe that when our people grow, we all succeed. Property Finder Guiding Principles Think Future First Data Beats Opinions, Speed Beats Perfection Optimise for Impact No Ostriches Allowed Our People, Our Power The Biggest Risk is Taking no Risk at All Find us at: Twitter Facebook Instagram Linkedin Glassdoor

Responsibilities

The Senior Data Engineer will design, build, and scale the core data infrastructure for analytics and AI solutions. This role involves owning end-to-end data pipeline development and ensuring the reliability and scalability of the data ecosystem.