Founded by Michael Lahyani in 2005 as a magazine (Al Bab World), Property Finder today is a single technology platform and brand across multiple countries in the MENA region. We offer the most advanced tools and best-in-class user experience for homeseekers, real estate brokers, and developers. Property Finder's most recent valuation secures our status among the Middle East's emerging unicorns, affirming a growth-oriented identity.
Over the years, we've expanded our operations to Bahrain, Egypt, Qatar, Saudi Arabia, and secured a strategic shareholding in Hepsiemlak, the leading property portal in Turkey. With over 600+ dedicated people in 6 regional offices, we facilitate more than 14 million monthly visits across our platforms, solidifying our position as a regional powerhouse in the proptech space.
As the pioneering portal for homeseekers in the region, we are on a mission to motivate and inspire people to live the life they deserve.
Position Summary:
We are seeking a Professional Data Engineer - Analytics to join our dynamic Data team, where you will play a crucial role in developing and maintaining robust data solutions.
As a Professional Data Engineer - Analytics you will drive innovation in analytics and uncover new perspectives and angles for analysis, serving as the critical link between raw data and actionable business insights. Your primary responsibility is to transform raw data into clean, organised, and accessible datasets that are optimised for analysis and decision-making.
This role combines data engineering, software development best practices, and business intelligence expertise to enable and empower data-driven decision-making across the organisation
The Data team operates with engineering precision, prioritising security, privacy, and regulatory compliance in every initiative. 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/Snowflake/Clickhouse
Platforms & Services: Docker, Kubernetes
PaaS: AWS ( DMS, Kinesis, Glue, Athena, S3 and others.)
ETL: FiveTran & DBT for transformation
Key Responsibilities:
- Apply Software Engineering Best Practices to Analytics Code: Analytics Engineers bring engineering rigor to analytics workflows by applying key software engineering principles
- Build Semantic Layers for Analytics: building and managing the semantic layer of an analytics stack, which serves as the bridge between raw data and end-user reporting tools
- Define Data Models: Design, implement, and maintain logical data models that represent business concepts, key metrics, and relationships, making data intuitive and easy to query.
- Establish Consistent Metrics: Create and enforce consistent definitions for business metrics and dimensions, reducing ambiguity and ensuring everyone in the organization works with a single source of truth
- Build a Self-Service Analytics Environment: create a self-service analytics culture, enabling business users to explore and analyse data independently without heavy reliance on data teams
- Automation and Pre-Built Templates: Develop reusable templates, automated reports, and pre-defined data models to streamline common analytical workflows.
- Data Governance and Security: Implement robust data governance frameworks, access controls, and data security measures to ensure data integrity, privacy, and compliance while enabling self-service capabilities
- Create User-Friendly and Innovative Dashboards for Business: An Analytics Engineer designs and develops intuitive, interactive, and visually appealing dashboards tailored to business needs
- Performance Optimization: Ensure dashboards are optimized for performance, handling large data volumes efficiently while providing real-time or near-real-time updates
- User-Centric Design: Work closely with business stakeholders to understand their requirements and craft dashboards that provide actionable insights with a clear and easy-to-navigate interface.
- Develop and maintain ETL pipelines using SQL and/or Python.
- Collaborate with cross-functional teams to understand and deliver data requirements.
- Use data transformation tools like DBT to prepare datasets to enable business users to self-service.
- Ensure data quality and consistency in all data stores.
- Monitor and troubleshoot data pipelines for performance and reliability.
Essential Experience:
- 7+ years of experience as a data engineer/ data Modelling.
- Extensive experience with BI tools such as Amazon QuickSight, Tableau, Qlik, Power BI, Looker .. for creating dashboards, reports, and data visualizations
- Proficiency in SQL.
- Proficiency in using programming languages such as Python or R for data manipulation, automation, and building data workflows.
- Experience with modern cloud data warehousing, data lake solutions like Snowflake, BigQuery, Redshift, Azure Synapse.
- Experience with ETL/ELT, batch, streaming data processing pipelines.
- Excellent ability to investigate and troubleshoot data issues, providing fixes and proposing both short and long-term solutions.
- Knowledge of AWS services (like S3, DMS, Glue, Athena, etc.)
- Familiar with dbt or other data transformation tools.
Other Desired Experience:
- Experience with Python
- Experience with orchestration tools like Dagster, Airflow, AWS Step functions, etc.
- Familiar with CI/CD pipelines and automation
- Experience with AWS services and concepts (like EC2, ECS, EKS, VPC, IAM, etc).
- Familiar with Terraform and Terragrunt.
- Experience with pub-sub, queuing, and streaming frameworks such as AWS Kinesis, Kafka, SQS, SNS.
Our promise to talent
We encourage our people, called creators, to move fast, to be bold and offer them countless ways to make an impact in a fast-growing and talent-centric organisation.
Our goal is to ensure that our people find their time at Property Finder a rewarding experience where the company’s growth also means personal growth.
Overall it is a place for you to be your best self.
Property Finder Principles
- Move fast and make things happen
- Data beats opinions
- Don’t confuse motion with progress
- Failure is success if we learn from it
- People over pixels
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