Professional Data Engineer
Property Finder is hiring a Professional Data Engineer in Cairo, Egypt. Level rates it ; you can apply on Property Finder.
Level's read on this job
- Works on AI ●●●○ is how Level rates this data engineer role: the daily work is on AI products, without building the model.
- The posting mentions LangChain, Bedrock and SQL among its tools and skills.
- Level classes it as a mid-level role.
- Property Finder has 40 other open roles on Level.
AI in this role
Develop and maintain robust ETL pipelines and data solutions integrated with cloud platforms and LLM frameworks.
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.
Use tools like Dagster/Airflow for pipeline orchestration Collaborate with cross-functional teams to understand and deliver data requirements. Ensure a consistent flow of high-quality data using stream, batch, and CDC processes. 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: 3+ years of experience as a data engineer. Proficiency in SQL. 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. Familiarity with GenAI, and how to leverage LLMs to resolve engineering challenges Other Desired Experience: Experience with AWS services and concepts (like EC2, ECS, EKS, VPC, IAM, etc). Familiar with Terraform and Terragrunt.
Experience with Python Experience with orchestration tools like Dagster, Airflow, AWS Step functions, etc. Experience with pub-sub, queuing, and streaming frameworks such as AWS Kinesis, Kafka, SQS, SNS. Familiar with CI/CD pipelines and automation.
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
Act Like An Owner
The Biggest Risk is Taking no Risk at All
Find us at:
Glassdoor
How we rate this
Professional Data Engineer at Property Finder rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data engineer jobs at this level
Pay
- Typical data engineer job
- $150k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
Questions you could be asked
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where data pipelines was part of your work. What did you do?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where infrastructure as code was part of your work. What did you do?
- Walk me through how you've used LangChain in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: ETL, Data Pipelines, Data Engineering, Infrastructure As Code, and LangChain. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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