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JuniorFull-timePythonJavaRustAWSGCPKubernetesDockerTerraformData ScienceCI/CD
Salary Range
$66,300 - $89,700 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
Property Finder is seeking a Data Engineer to build reliable, scalable data pipelines that support analytics, AI/ML, and Generative AI initiatives. You will work closely with senior engineers and data scientists to deliver high-quality solutions that drive business growth.
Key Responsibilities
- →Build and maintain batch and streaming data pipelines with a focus on reliability, performance, and cost efficiency.
- →Develop SQL, Python, and Spark/PySpark transformations to support analytics, reporting, and ML workloads.
- →Contribute to data model design and ensure datasets adhere to high standards of quality, structure, and governance.
- →Support integrations with internal and external systems, ensuring accuracy and resilience of data flows.
- →Build and maintain data flows that support Generative AI workloads, including embedding generation and vector pipelines.
- →Collaborate with ML/GenAI teams to enable high-quality training and inference datasets.
- →Work with Data Science, Analytics, Product, and Engineering teams to translate data requirements into reliable solutions.
- →Uphold strong data quality, testing, and documentation standards.
- →Support deployments, troubleshooting, and operational stability of the pipelines you own.
Requirements
- ✓5+ years of experience as a Data Engineer.
- ✓Strong SQL and Python skills; good understanding of Spark/PySpark.
- ✓Experience building and maintaining production data pipelines.
- ✓Practical experience working with cloud-based data warehouses and data lake architectures.
- ✓Experience with AWS services for data processing (Glue, Athena, Kinesis, Lambda, S3, etc.).
- ✓Familiarity with orchestration tools (Dagster, Airflow, Step Functions).
- ✓Solid understanding of data modeling and data quality best practices.
- ✓Experience working with CI/CD pipelines or basic automation for data workflows.
- ✓Exposure to Generative AI workflows or willingness to learn: embeddings, vector stores, enrichment pipelines, LLM-based data improvements, retrieval workflows.
