Professional Data Engineer Property Finder Group

Key Responsibilities

• Build and maintain batch and streaming data pipelines with strong emphasis on reliability, performance, and efficient cost usage.
• 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.

GenAI & Advanced Data Use Cases

• Build and maintain data flows that support GenAI workloads (e.g., embedding generation, vector pipelines, data preparation for LLM training and inference).
• Collaborate with ML/GenAI teams to enable high-quality training and inference datasets.
• Contribute to the development of retrieval pipelines, enrichment workflows, or AI-powered data quality checks.

Collaboration & Delivery

• Work with Data Science, Analytics, Product, and Engineering teams to translate data requirements into reliable solutions.
• Participate in design reviews and provide input toward scalable and maintainable engineering practices.
• Uphold strong data quality, testing, and documentation standards.
• Support deployments, troubleshooting, and operational stability of the pipelines you own.

Professional Growth & Team Contribution

• Demonstrate ownership of well-scoped components of the data platform.
• Share knowledge with peers and contribute to team learning through code reviews, documentation, and pairing.
• Show strong execution skills delivering high-quality work, on time, with clarity and reliability.

Impact of the Role

In this role, you will help extend and strengthen the data foundation that powers analytics, AI/ML, and GenAI initiatives across the company. Your contributions will improve data availability, tooling, and performance, enabling teams to build intelligent, data-driven experiences.

Tech Stack

• Languages: Python, SQL, Java/Scala
• Streaming: Kafka, Kinesis
• Data Stores: Redshift, Snowflake, ClickHouse, S3
• Orchestration: Dagster (Airflow legacy)
• Platforms: Docker, Kubernetes
• AWS: DMS, Glue, Athena, ECS/EKS, S3, Kinesis
• ETL/ELT: Fivetran, dbt
• IaC: Terraform + Terragrunt