Data Engineer (Data Pipelines & RAG)

Our client is a fast growing Property Tech AI company About the role They are seeking a versatile Data & AI Engineer to build, deploy & maintain end-to-end data pipelines for downstream Gen AI applications.

You'll design data models and transformations, build scalable ETL/ELT workflows, while learning fast and working on the AI agent space.

Key Responsibilities Data Modeling & Pipeline development Automate data ingestion from diverse sources (Databases, APIs, files, Sharepoint/ document management tools, URLs).

Most files are expected to be unstructured documents with different file formats, tables, charts, process flows, schedules, construction layouts/drawings, etc.

Own chunking strategy, embedding, indexing all unstructured & structured data for efficient retrieval by downstream RAG/agent systems Build, test, and maintain robust ETL/ELT workflows using Spark (batch & streaming) Define and implement logical/physical data models and schemas.

Develop schema mapping and data dictionary artifacts for cross-system consistency Gen AI Integration Instrument data pipelines to surface real-time context into LLM prompts Implement prompt engineering and RAG for varied workflows within the RE/Construction industry vertical Observability & Governance Implement monitoring, alerting, and logging (data quality, latency, errors) Apply access controls and data privacy safeguards (e.g., Unity Catalog, IAM) CI/CD & Automation Develop automated testing, versioning, and deployment (Azure DevOps, GitHub Actions, Prefect/Airflow) Maintain reproducible environments with infrastructure as code (Terraform, ARM templates) Required Skills & Experience 5 years in Data Engineering or similar role, with at least 12-24 months of exposure to building pipelines for unstructured data extraction including document processing with OCR, cloud-native solutions and chunking, indexing etc. for downstream consumption by RAG/ Gen AI applications.

Proficiency in Python, dlt for ETL/ELT pipeline, duckDB or equivalent tools for analytical in-process analysis, dvc for managing large files efficiently.

Solid SQL skills and experience designing and scaling relational databases.

Familiarity with non-relational column based databases is preferred.

Familiarity with Prefect is preferred or others (e.g.

Azure Data Factory) Proficiency with the Azure ecosystem.

Should have worked on Azure services in production.

Familiarity with RAG indexing, chunking and storage across file types for efficient retrieval.

Strong Dev Ops/Git workflows and CI/CD (CircleCI / Azure DevOps) Experience deploying ML artifacts using MLflow, Docker, or Kubernetes is good to have.